Background of the Invention
[0001] The invention relates to the detection, enumeration, and identification of replicating
cells, especially microbial cells (
e.
g., bacteria, yeasts, and molds), in medical, industrial, and environmental samples.
Microbial culture is the predominant methodology in these markets, because of its
many attractive features. The invention addresses the chief drawback of microbial
culture - the length of time needed to achieve results - while retaining the beneficial
attributes of the method.
Microbial culture for detecting and enumerating microbes
[0002] During the 19th and 20th centuries an understanding emerged concerning the role of
bacteria, yeast, and molds in causing infectious diseases and determining the quality
of foods and beverages. Early on, a powerful method, microbial culture, was developed
for detecting small numbers of microbes. Microbial culture allows simple visual detection
of microbes by exploiting their propensity to reproduce in large numbers rapidly.
For example, a single bacterial cell, which is much too small to see by eye (about
one millionth of a meter), when placed in nutrient broth, can cause the broth to become
visibly cloudy in less than 24 hours.
[0003] A related microbial culture technique, called microbial enumeration or colony counting,
quantifies the number of microbial cells in a sample. The microbial enumeration method,
which is based on
in situ microbial replication, generally yields one
visually detectable "colony" for each microbial cell in the sample. Thus, counting the visible colonies
allows microbiologists to determine the number of microbial cells in a sample accurately.
To perform microbial enumeration, bacterial cells can be dispersed on the surface
of nutrient agar in petri dishes ("agar plates") and incubated under conditions that
permit
in situ bacterial replication. The individual,
visually undetectable, microbe replicates repeatedly to create a large number of identical daughter microbes
at the physical site where the progenitor microbial cell was deposited. The daughter
cells remain co-localized (essentially contiguous) with the original cell, so that
the cohort of daughter cells (which may grow to tens or hundreds of millions of cells)
eventually form a visible colony on the plate.
[0004] Electronic methods have been developed for enumerating microbial colonies. Most such
methods automate colony counting but do not substantially increase the sensitivity
or decrease the time to results compared to traditional enumeration by eye. Colony
counters use a variety of optical methods for detecting colonies including detection
of intrinsic optical properties of microcolonies (
e.
g.,
U.S. Patent No: 3,493,772;
U.S. Patent No: 3,811,036;
U.S. Patent No: 5,290,701;
Arkin, A. P., et al. (1990); Biotechnology (N Y) 8: 746-9) and color changes of pH indicator molecules in the matrix surrounding the colonies
(
U.S. Patent No. 5,510,246). Methods that use stains or probes to label the colonies have also been developed
and will be discussed below.
[0005] Microbial culture is a remarkably successful method, as evidenced by the fact that
even after more than a century, the method still dominates medical microbiology and
quality control testing in industrial microbiology (
e.
g., pharmaceutical, food, and beverage manufacturing). The method is inexpensive, relatively
simple, and ultra-sensitive. The sensitivity of microbial culture can be seen in the
common test for foodborne pathogens in ground beef. A single microscopic bacterial
pathogen cell can be detected in 25 grams of ground beef using microbial culture.
Another advantage of microbial culture is its ability to detect a large range of microbes
of medical and industrial significance.
[0006] An advantage of
in situ bacterial replication is the ability to generate a pure, or clonal, population of
cells (called pure cultures, clones, or colonies). A pure culture is a large collection
of identical living cells which descend from the same progenitor cell. Pure cultures
are required for methods that identify microbes and for determining antibiotic resistance.
Medical microbiology relies heavily on pure cultures, since bacterial pathogens are
frequently isolated from non-sterile clinical samples (
e.
g., feces or wounds) along with non-pathogenic bacteria that are likely to be even
more numerous than the pathogenic cell. Isolating pure microbial cultures is also
important in industrial microbiology. For example, pharmaceutical and cosmetics manufacturers
must test their products for the presence of microbial contaminants. Pure cultures
of the contaminating microbes are used for microbial identification, which determines
whether a production batch must be discarded and aids in investigating the source
of the contamination in the industrial process.
Table 1.
Microbial enumeration using microbial culture |
Advantages |
• ultra-sensitive |
• quantitative |
• generates pure cultures |
• can detect and enumerate many types of microbes in a single test |
• can selectively grow microbes |
• only detects replicating cells |
• inexpensive |
• simple and easy to perform |
Disadvantages |
• slow |
• manual procedures and analysis |
• not all microbes are culturable |
[0007] The ability to culture microbes
selectively is an essential tool for microbial identification and for determining resistance
and susceptibility to antimicrobial agents such as antibiotics. Selective culture
exploits the fact that different microbes require different growth conditions. These
differences arise from the fact that strains of microbes differ in their biochemical
makeup because of inherent genetic differences. For example, one type of microbe might
be able to grow on nutrient medium containing the sugar sorbitol as the sole source
of carbon atoms to fuel its growth, while another type of microbe cannot. Selective
growth is important in the food industry. For example, a food sample can be scanned
for a particular food pathogen,
Salmonella, by plating the sample on media that allows
Salmonella to grow but not other food microbes.
[0008] Similarly, selective culture is used to determine which antibiotic is most effective
for killing a bacterial strain isolated from the spinal fluid of a child with bacterial
meningitis. A pure bacterial culture (derived from a clonal colony from a nutrient
agar plate) is used to inoculate growth medium containing various antibiotics at various
concentrations. The optimal antibiotic therapy is determined by monitoring the ability
of the microbe to grow in the presence of the various antibiotics. Determining antibiotic
resistance and susceptibility by selective growth on the surface of solid nutrient
agar medium is another common approach. For example, in the Kirby-Bauer method, small
filter disks impregnated with different antibiotics are placed on the surface of nutrient
agar plates coated with a pure culture of bacteria from a clinical sample. A gradient
of antibiotic diffuses radially outward from the filter. Bacteria that are resistant
to high levels of the antibiotic grow up to the edge of the filter. However, bacteria
that are very sensitive to the antibiotic can not grow unless they are far from the
edge of the filter. After incubating the plates (usually for one or two days) a microbiologist
determines the level of resistance to an antibiotic by measuring the thickness of
the growth-free ring or zone around the filter. A related method, the "E" test (Hardy
diagnostics), uses a rectangular strip that is impregnated with a gradient of antibiotic.
The level of bacterial resistance is determined by measuring the point on the strip
with the highest antibiotic concentration next to which the bacteria continue to replicate.
[0009] The most serious drawback of microbial culture is that it is slow - it takes time
to generate the number of cells required for visual detection. The long growth period
required for microbial culture is a significant problem in both healthcare and industry.
For example, because it requires days to culture and identify the microbe causing
a patient's blood infection, a patient with a fungal blood infection could die before
antifungal therapy is even begun. Some infectious agents, such as the bacterium that
causes tuberculosis, generally require weeks to grow in culture. The long time required
for detecting
M tuberculosis can result in a patient with tuberculosis infecting many others with the highly contagious
disease or the costly quarantine of patients who do not have tuberculosis.
[0010] In food manufacture, long testing cycles can increase food spoilage or result in
moving inadequately tested material through subsequent processing steps. Slow microbial
culture also adversely impacts the production of biopharmaceuticals and vaccines.
In these applications, the manufacturing process often requires pooling of batches.
Because of long microbial culture testing cycles and the need to move material through
the manufacturing process, contaminated batches are sometimes not detected until after
a batch pooling step. If it is subsequently found that a contaminated batch was combined
with uncontaminated batches, the whole pool of combined batches must be discarded.
[0011] Other disadvantages of microbial culture, such as tedious manual procedures and inability
to culture some microbes, are considered less problematic than the long time required.
For example, manual methods for microbial enumeration predominate, even though instruments
for automated plating and analysis have been introduced. Most types of microbes found
in the environment cannot be grown in the laboratory. However, such microbes are often
not harmful to humans or are destroyed in industrial manufacturing processes and are
therefore ignored for most applications. However, several important exceptions of
critical medical importance include hard or impossible to culture bacteria such as
Chlamydia, strains of which can cause sexually transmitted disease and pneumonia. Fortunately,
alternative culture-independent methods are available in these cases (see below).
Rapid microbial culture enumeration methods
[0012] A number of microbial culture methods for more rapid microbial enumeration have been
developed. (e.g.
U.S. Patent No: 4,587,213). One rapid microbial culture method deposits bacterial cells on microscope slides
coated with nutrient medium. Using microscopic examination, microbial growth can be
detected much earlier than with the naked eye, since microscopes can detect microcolonies
resulting from a small number of cell divisions. However, this method is not effective
for testing large samples containing low numbers of microbial cells, because only
a very small volume of sample can be observed in a microscopic field of view. The
low sensitivity of microscopic methods generally limits their usefulness to samples
containing more than ten thousand bacterial cells per milliliter - these methods are
much less sensitive than traditional microbial culture.
[0013] The advent of electronic imaging systems has led to the development of numerous automatic
"colony counters." Although, most of these counters are designed to aid the user by
automating the colony counting process and do not decrease the time to result, some
systems have demonstrated the ability to detect colonies before they are large enough
to be seen easily by eye. For example, the Colifast Rapid Microcolony Counter (Colifast)
can detect small fluorescently labeled colonies of coliform bacteria hours before
they can be seen by eye. The Colifast system achieves enhanced detection by using
a fluorogenic compound (a substance that is not fluorescent until metabolized by coliform
bacteria) included in the nutrient agar media.
[0014] A system for rapid enumeration of microbial colonies using bioluminescent labeling
has recently been commercialized. The MicroStar system (Millipore) uses the cellular
ATP in microcolonies to generate light via the action of applied luciferase enzyme
and substrates. The method reduces time to detection substantially. The MicroStar
imaging system has also been used in conjunction with labeled probes to identify specific
bacteria (
Stender, H., et al. J Microbiol Methods 46: 69-75 (2001)). A drawback of the system is that the detection method kills the microbes, precluding
isolation of pure cultures from the colonies. The system also requires an expensive
image intensifier module.
[0015] An instant film-based method for detecting microcolonies containing specific bacteria
has been developed by Boston Probes (
Perry-O-Keefe, H., et al. Journal of Applied Microbiology 90: 180-9 (2001)). Microbial microcolonies on membranes are labeled using microbe-specific PNA probes
tagged with an enzyme capable of generating a chemiluminescent signal. The membranes
are then placed on X-ray or instant-film for imaging. The method is limited to scanning
for a particular microbe in one experiment. A similar method uses fluorescently labeled
PNA probes and an array scanner (
Stender, H., et al. Journal of Microbiological Methods 45: 31-9 (2001)). These approaches require substantially more expertise than traditional culture
methods.
Rapid microbial enumeration without microbial culture
[0016] The fastest methods for microbial enumeration forgo microbial culture. Medical and
industrial microbiologists are generally interested only in enumerating viable microbes
- only living microbes are capable of replicating during microbial culture. Therefore,
to be most effective, methods that detect individual cells without reliance on cellular
replication must distinguish living from dead microbes by using physiological surrogates
for cellular replication (
e.
g.,
Nebe-von-Caron, G., et al., J Microbiol Methods 42: 97-114., 2000;
Mignon-Godefroy, K., et al., Cytometry 27: 336-44, 1997). Cells are stained with dyes that measure a biochemical property that is generally
correlated with the
ability to replicate (
e.
g., esterase activity or biochemical respiration). Validating and instituting surrogate
methods have been problematic since samples that are known to meet regulatory standards
and that are scored as sterile using traditional plate culturing methods often have
thousands of cells that score positive for the surrogate biochemical activity.
[0017] An example of a system that directly detects viable cells is the ScanRDI system (Chemunex).
ScanRDI enumerates microbial cells that are stained with a fluorogenic esterase substrate
using laser scanning technology (
U.S. Patent No: 5,663,057;
Mignon-Godefroy, K., et al., Cytometry 27: 336-44, 1997). A laser-scanning system (including an optical collection system using photomultiplier
tubes (PMTs)) captures an image of the filter and can detect individual labeled cells.
The system illuminates and queries a
microscopic area (generally 4-14 µm) but scans the beam progressively so as to cover a macroscopic
area (
e.
g., a 25 mm diameter circle). The system is designed to detect cells with intact membranes
and active esterase enzyme. There is a correlation between the numbers of such cells
and the number of cells that can form colonies on growth medium. However, this approach
often results in substantial "overcounting" -
i.
e., higher numbers of cells than are detected by traditional culture (
Costanzo, S., et al. (2002). PDA Journal of Pharmaceutical Science and Technology
56: 206-219). Another disadvantage of the ScanRDI system is that it kills the microbes during
the staining process precluding generation of pure cultures from the detected microbes.
Finally, laser scanning systems for cellular enumeration are complex and expensive
(hundreds of thousands of dollars) making them difficult to justify for routine microbiological
applications. Other laser scanning systems have also been commercialized (
Miraglia, S., et al., J Biomol Screen 4: 193-204, 1999;
Tibbe, A. G., et al., Nat Biotechnol 17: 1210-3, 1999;
Kamentsky, L., 2001, Laser Scanning Cytometry. In Cytometry, Z. Darzynkiewicz, H.
Crissman and J. Robinsnon, eds. Methods in Cell Biology Vol. 63, Part A, 3rd ed, Series
Eds. L. Wilson and P. Matsudaira. (San Diego: Academic Press)).
[0018] Flow cytometry is another powerful method that can rapidly enumerate microbes without
relying on cellular replication (
Alvarez-Barrientos, A., et al., Clin Microbiol Rev 13: 167-195, 2000). Individual organisms or particles are forced to flow through a narrow channel,
one at a time, past a laser beam. Besides enumeration, information about size/shape
and composition is gathered by analyzing the fluorescence emission and light scattering
caused by the organisms. Thousands of individual cells or particles can be analyzed
per minute. Pathogens can by identified using flow cytometry by binding fluorescently
labeled species-specific antibodies or nucleic acid probes to fixed organisms (Alvarez-Barrientos,
2000,
supra).
[0019] Pathogens can by identified using flow cytometry by binding fluorescently labeled
species-specific antibodies or nucleic acid probes to fixed organisms (Alvarez-Barrientos,
2000,
supra). Individual cells of one particular type are usually the targets. Flow cytometric
methods have been used more extensively for quantitatively detecting particular cell
types on the basis of the ability to bind labeled probes, usually either antibodies
or nucleic acids. For example, flow cytometry is used to quantify the population sizes
of classes of lymphocytes in patients with AIDS. Flow cytometry is a more complex
and expensive method than traditional culture. Although faster than traditional culture,
flow cytometry does not have a comparable limit of detection to the traditional method.
Traditional microbial culture can detect one bacterial cell in 0.1 liter of water,
while flow cytometry is most effective when there at levels that are many thousands
of times higher than that. Furthermore, microbial targets are often killed by the
staining methods used for detection, eliminating the ability to produce pure cultures.
[0020] Using microscopic imaging to visualize and enumerate microorganisms directly can
be rapid and relatively simple to perform (
Amann, R.I., et al., Microbiological Reviews 59: 143-69, 1995). Direct fluorescent assays (DFA) in which a fluorescently labeled antibody reacts
with a fixed sample is a common method in clinical diagnostics laboratories. For example,
specimens suspected of containing bacterial agents are routinely stained with Gram
stain. Similarly, to test for
M tuberculosis, samples are subjected to acid fast staining. The drawback of this technique is that
it is many thousands of times less sensitive than microbial culture. The low sensitivity
is due to the small fields visualized at high magnification. Only at high target cell
concentrations are small fields likely to contain a target cell. Thus, for example,
reliable identification of bacterial pathogens in sputum using fluorescent
in situ hybridization requires titers of about 4 x 10
5 cells/ml or more. Clinical samples obtained in common medically significant infections
may contain fewer than 100 cells/ml - a concentration that is not nearly high enough
to expect to find a cell in a high power microscopic field.
[0021] A system that does have the sensitivity to detect single bacterial cells using large
area non-magnified imaging has been developed by researchers at Hamamatsu Corporation
(
Masuko, M., et al., FEMS Microbiol Lett 67: 231-8, 1991;
Masuko, M., et al., FEMS Microbiol Lett 65: 287-90, 1991;
Yasui, T., et al., Appl Environ Microbiol 63: 4528-33, 1997). Large area imaging of individual microscopic target cells is accomplished using
an ultrasensitive photon-counting CCD camera coupled to a fiber optic system, image
intensifier, and Image-Processor. A disadvantage of this system is the great expense
incurred because of the incorporation of the image intensifier and associated optics.
[0022] Furthermore, unlike microbial culture methods, the system can not detect any microbe,
distinguish between living and dead microbes, or generate pure cultures.
Rapid microbial enumeration by quantifying molecular constituents of cells
[0023] Numerous methods for detecting and identifying microbes based on their molecular
constituents have been developed in the last half-century. Although some of these
methods are substantially faster than microbial culture, none offers all of the features
of culture that are critical to microbiologists. For example, although numerous immunoassays
for microbes have been commercialized, this technique is not inherently quantitative,
is much less sensitive than microbial culture, and is not as powerful as culture for
detecting many types of microbes in a single test. Or, as another example, nucleic
acid amplification methods can be as sensitive as microbial culture, but they do not
distinguish between living and non-living cells and can not deliver pure cultures
for antibiotic susceptibility testing. Methods for biochemical analysis (
e.
g., of fatty acids, nucleic acids, or proteins) using electrophoresis, mass spectroscopy,
and chromatography can be powerful for microbial identification, but such methods
are usually inappropriate for microbial enumeration and are generally too expensive
and complex for routine microbial diagnostics.
Unmet needs for microbial enumeration
[0024] In summary, current microbial enumeration testing is dominated by microbial culture.
Microbial culture has the important advantages of being simple, ultra-sensitive, inexpensive,
and quantitative but has the significant drawback of being slow. The long time required
for results has major costs in healthcare and in manufacturing. More rapid methods
have been developed, but while improving the time to results, they have sacrificed
one or more of the critical advantages of microbial culture.
[0025] Thus, there is need for a test that is faster than traditional microbial culture
but that retains the key benefits of the traditional method.
Summary of the Invention
[0026] The invention enable efficient, rapid, and sensitive enumeration of living cells
by detecting microscopic colonies derived from
in situ cell division using large area imaging. Microbial enumeration tests based on the
invention address an important problem in clinical and industrial microbiology - the
long time needed for detection of traditional tests - while retaining key advantages
of the traditional methods based on microbial culture. Embodiments of the invention
include non-destructive aseptic methods for detecting cellular microcolonies without
labeling reagents. These methods allow for the generation of pure cultures which can
be used for microbial identification and determination of antimicrobial resistance.
[0027] The invention features a method for detecting living target cells in a sample, said
method comprising the steps of:
- (a) providing living target cells present in said sample in a detection zone comprising
a detection area at a density of less than 100 target cells per mm2 of the detection area, wherein within said detection zone said cells are randomly
dispersed and immobilized;
- (b) allowing the formation of one or more microcolonies of said target cells by in
situ replication; and
- (c) detecting said one or more microcolonies;
wherein the longest linear dimension of said detection area is greater than 1 mm;
said one or more microcolonies have a mean measurement of less than 50 microns in
at least two orthogonal dimensions; said detecting does not entail magnification of
more than 5x; said detecting detects a property of said one or more microcolonies
that does not depend on the addition of a signaling moiety or category binding molecule;
and said cells in said one or more microcolonies remain competent to replicate following
said detecting.
Advantages of the invention
[0028] Some advantages of various embodiments of the invention are listed in Table 2.
Table 2
Embodiment |
Advantages |
• Reagent-less fluorescent detection and enumeration of microcolonies |
- Minimal changes to accepted practices
- Faster and lower risk regulatory path
- Low cost of goods
- System simplicity
- Enables non-destructive testing (below)
|
• Collection optics optimized for detecting living microcolonies |
|
• Non-magnified large area imaging of individual live microcolonies on membranes |
- Allows ultra-sensitive detection
- Allows large dynamic range
- Allows broad range of sample volumes
- High signal:background ratio at low titers
|
• Non-destructive enumeration (i.e., microbes are not killed) |
- Allows generation of pure cultures
- Allows microbial identification
- Allows detection of antimicrobial resistance
- Allows internal validation (below)
|
• Internal comparison with traditional visible colonies |
- Streamlines demonstration of equivalence to validate methods
|
• Imaging live microcolonies in sterile (closed) disposable |
- Allows multiple reads
- Minimizes false positives
|
• Methods & software uniquely discriminate growing microbes from artifacts |
- Added detection robustness, specificity
- Allows detection in complex samples
|
[0029] The invention's short time needed to achieve results derives from the invention's
ability to detect microcolonies containing only a small fraction of the cells that
are required by the traditional methods. Since cell replication requires time, detecting
small microcolonies using the invention provides results faster than detecting the
large visible colonies using traditional enumeration methods. To detect small microcolonies
the invention uses a combination of efficient signal generation and signal detection
methods.
[0030] The ultra-sensitivity - its ability to detect small numbers of microscopic cells
in large samples - stems, in part, from the use of large area imaging. For example,
the invention can detect microscopic colonies
without magnification. This feature allows a large area to be surveyed for microcolonies in a single image.
Imaging a large area is a key to the invention's ability to efficiently analyze large
sample volumes. For example, the microbial contaminants in a large volume of a sample
can be deposited on a membrane using membrane filtration. The invention using large
area non-magnified imaging of microcolonies can analyze the entire membrane efficiently.
In contrast, using a high magnification microscope to evaluate the microcolonies on
the same filter might require thousands of images.
[0031] The power to enumerate small numbers of microcolonies in a large area efficiently
also comes from the invention's ability to use imaging approaches that compare object
signals to local backgrounds. This ability improves the signal to background ratio
for samples containing few cells over methods that integrate the total signal and
background in a large area.
[0032] Assay robustness for samples with few cells is provided by the invention's inherent
ability to enumerate growing microcolonies. Thus, the invention can decrease false
positives over methods which detect a single integrated signal, such as methods that
quantify the presence of biomolecules (
e.
g., ATP, antigens, or nucleic acids). Any artifact that causes a signal can generate
a false positive when using methods that rely solely on integrated signal. Consider
a sample that contains 482 microbial cells each of which generate 100 fluorescent
units. The result of an integrative method is a single number (48,200 fluorescent
units). Artifacts that generate a similar number of fluorescent units, for example,
a large fluorescent dust particle may be indistinguishable. The invention, however,
can easily distinguish between a single large dust fluorescent dust particle and 482
individual growing microcolonies.
[0033] Detecting growing microcolonies is a powerful method for discriminating against false
positive signals from inanimate objects and cells incapable of growth under the test
conditions. For example, consider a test to detect microbial microcolonies on a membrane
lying on solid growth media in a petri dish. In one embodiment of the invention, the
detection area is imaged before allowing the microbes in the detection area to grow
into microcolonies. If some fluorescent dust particles or autofluorescent mammalian
cells are present in the detection area some positive signals will be apparent in
this "zero time" image. After incubating the petri dish to allow for microbial replication
another image is taken. When the two images are aligned in register, the positive
signals that correspond to microcolonies can be distinguished from the false positives
since the false positives are present (usually unchanged) in the "zero time" image
and the post-incubation image. Only growing microcolonies should appear over time.
To confirm the microcolony signals, images can be acquired and compared at multiple
time points during the incubation. Only growing microcolonies should increase in signal
strength and in size over time.
[0034] Tests constructed using the invention can have a large dynamic range compared to
tests constructed using methods in the prior art. Thus, for example, a test based
on the invention designed can detect from one to 10
6 microcolonies in a single image. In contrast, traditional microbial enumeration methods
work best when about 30 to 150 colonies are deposited on a filter (47 mm diameter).
New enumeration methods (
e.
g., Chemunex's ScanRDI and Millipore's MicroStar) also have limited dynamic ranges.
[0035] To achieve efficient signal generation, the invention can exploit either the intrinsic
optical properties of the microcolonies (
e.
g., autofluorescence, reflectance, or light scattering) or various externally applied
labeling reagents. The ability to exploit a range of optical properties and labeling
methods enables creation of important microbiological tests. For example, using a
method that detects a ubiquitous property of microcolonies (
e.
g., autofluorescence or infrared absorption) is useful for tests that enumerate total
microbial content of a sample. Such tests are critical in food processing for determining
the likelihood of spoilage and for finished product release testing in pharmaceutical
manufacture. One important embodiment of the invention uses a reagent-less system
based on detecting cellular autofluorescence to detect small microbial microcolonies.
This embodiment provides a simple, non-destructive, aseptic approach to microbial
enumeration. To detect specific types of cells, category-specific labeling reagents
can be used. For example, a fluorescently labeled antibody that specifically binds
to
Listeria monocytogenes can be used to detect microcolonies derived from cells of this important food pathogen.
[0036] Like traditional microbial culture, the invention can exploit the diagnostic power
of measuring microbial growth under selective conditions. For example, to determine
bacterial resistance to antibiotics bacteria can be grown on growth medium onto which
antibiotic disks have been placed. The size of the no-growth zone near the disks determines
antibiotic resistance. The invention can be used to detect the size of this zone more
rapidly. Similarly, the invention can be used to detect the growth of specific microbes
on selective medium rapidly.
[0037] Simplifying the obligatory test validation cycle in which a new method is shown to
be equivalent to the "gold standard" method is another advantage of the invention
that derives from non-destructive enumeration. The invention facilitates equivalence
to the "gold standard" culture tests by allowing an internal comparison of the new
and old methods. Briefly, after imaging the microcolonies derived from microbes in
a sample at an early time point, the samples can be re-incubated for the amount of
time required when using traditional visual detection of colonies. In this way an
internal comparison can be made between the invention's enumeration of the microcolonies
and the enumeration of
the same colonies at a later time by the traditional method.
[0038] Other features and advantages will be apparent from the following description and
the claims.
[0039] By
target cell is meant a cell that is potentially present in a sample and whose presence is assayed
by the invention.
[0040] By
category of target cells is meant multiple target cells that are considered identical for
the purposes of a test constructed using the invention.
[0041] Consider a test designed to detect
any strain of
E. coli bacteria. For the purposes of the test, the category "
E. coli" would thus include any bacterium in the species
E.
coli. Such a test would be designed to detect, without differentiation, any bacterium in
the species
E. coli. Bacteria, and other target cells, that are not
E.
coli would either not be detected in this test, or would be detected and identified as
not being members of the group
E.
coli. In contrast, consider a test designed to detect the pathogen
E.
coli O157:H7, a subgroup of the
E. coli species. In this case, the subgroup
E. coli O157:H7 is a category of target cells. Bacteria in the subgroup,
i.
e., in the category "
E. coli O157:H7", are detected without differentiation.
E. coli that are not in the
E. coli O157:H7 subgroup are not detected by the test and are therefore not in the
E. coli O157:H7 category.
[0042] Categories need not be taxonomically related as in the previous paragraph. For example,
a test might be designed to detect the category of bacteria that makes a protein that
is required to confer resistance to the antibiotic vancomycin. This protein could
be made by bacterial strains that are not closely related,
i.
e., that are members of disparate species. A vancomycin resistant strain in one species,
however, is likely to be very closely related to vancomycin sensitive strains in the
same species. The category of bacteria that make the vanA protein (important for achieving
vancomycin resistance), for instance, includes vancomycin-resistant bacteria in the
genus
Enterococcus and in the genus
Staphylococcus, while the majority of enterococci and staphylococci are not included in the category.
Thus, in this case, it can be seen that the category encompasses target cells that
are considered, for the purposes of the test, to be identical because of a common
feature, in this case a molecular component (a
category-specific binding site) rather than to a common phylogenetic (genealogical) relationship.
[0043] By
non-overlapping categories of target cells is meant sets of target cells whose union is the null set. That is,
the category of all
E. coli bacteria, the category of all bacteria in the genus
Pseudomonas, and the category of all fungi are non-overlapping categories. That is, no member
of any of the categories is a member of any of the other sets.
[0044] By the
categorical complexity of a test is meant the number of non-overlapping categories that are detected in
the test.
[0045] By a
category-specific binding site is meant a site on a target cell that
specifically binds to a category-binding molecule under
specific-binding conditions and that distinguishes target cells that are members of a particular category to
be identified in a test from target cells that are not members of that category but
might also be present in the test sample. That is, the site is present typically on
all members of one category, and typically not on any members of non-overlapping categories.
Category-specific binding sites
specifically bind to
category-specific binding molecules.
[0046] If a test scans a sample for a category of target cells that constitutes a taxonomic
group, a category-specific binding site is one that is present in essentially all
members of that taxonomic group, but is not present in essentially all members of
other taxonomic groups that might be present in the test sample.
[0047] Alternatively, a test might scan a sample for category-specific binding sites that
are shared by members of different taxonomic groups. Examples of this type of category-specific
binding site include various macromolecules (
e.
g., DNA) and genes, mRNAs, and proteins that confer antibiotic resistance, confer virulence,
or indicate viability. A category-specific binding site is often a part of a larger
molecule or complex. For example, a category-specific genomic sequence can be used
as a category-specific binding site in a test. Such a category-specific binding site
is part of a much larger genome that contains (
1) sections that are not category-specific; (
2) sections that are category-specific binding sites but for which the test does not
scan; and (
3) other sections that are distinct category-specific sequences for which the test
does scan.
[0048] Binding sites that are present, e.g., in 80%, 90%, 95%, or more than 99% of the target
cells that are members of a category but that are absent, e.g., in 80%, 90%, 95%,
or more than 99% of the target cells that are members of all other categories of the
same class, are considered category-specific binding sites. Note that a category-specific
binding site can be trivially or exceptionally absent from a target cell that is a
member of the category. Similarly, a category-specific binding site can be trivially
or exceptionally present in a target cell that is not a member of a category. For
example, consider a protein site that occurs in essentially all
E.
coli bacteria but in no other bacterial species. If, as might be the case in less than
one cell out of millions of bacteria, a mutation causes the protein not to be produced,
the marker will not be present in that strain of
E. coli. However, this protein site is still considered a category-specific binding site.
Alternatively, the gene for the same protein is transferred to a strain of a different
species of bacteria by recombinant DNA technology or by natural means (
e.
g., by viral transduction). In this case, a bacterial strain that is not a member of
the category
E. coli would express what would still be considered an
E.
coli-specific binding site.
[0049] By
category-binding molecule is meant a molecule or molecular complex that
specifically binds to a category-specific binding site. Examples of category-binding molecules are nucleic
acid probes that hybridize to genomic DNA; nucleic acid aptamers that have been selected
or "evolved"
in vitro to bind specifically to sites on proteins; antibodies that bind to cellular antigens
or serum proteins; and ligands such as epidermal growth factor or biotin that bind
specifically to hormone receptors or to binding molecules, such as avidin. Two category-binding
molecules are said to be distinct if they bind to distinct and non-overlapping category-specific
binding sites. Category-binding molecules may be referred to according to their molecular
composition,
e.
g., a category binding oligonucleotide, probe, antibody, ligand,
etc.
[0050] By a category-binding molecule that
specifically binds to a category of target cells is meant a category-binding molecule that binds under
defined
binding conditions to essentially all target cells that are members of a category scanned for by a test,
but to essentially no target cells that are not members of the category but that are
likely to be present in the sample. The number of category-binding molecules that
are bound by target cells in a category scanned for as compared to the number bound
by target cells not in such a category, are typically two-fold, five-fold, ten-fold,
or greater than fifty-fold greater.
[0051] By
binding conditions is meant the conditions used in a test to achieve specific binding of category-binding
molecules to category-specific binding sites. For example, when the category-binding
molecules are category-specific DNA probes, the binding conditions for a particular
test might be stringent DNA hybridization conditions. The appropriate stringent DNA
hybridization conditions depend on the nature of the probes, as is well known by those
familiar with the art. For example, for typical DNA probes of length greater than
500 bases, an appropriate binding condition (usually referred to as a "washing condition"
in the hybridization vernacular) is 65°C at 0.2X SSC. For binding an antibody to an
antigen, typical binding conditions are room temperature in PBS-TB.
[0052] By a
family of category-binding molecules is meant a set of category-binding molecules that specifically
bind to a particular category of target cells.
[0053] Polyclonal antibodies generally constitute families of category-binding molecules
since they generally comprise multiple distinct category-binding molecules that bind
to the same category of target cell. Note that, unless affinity purification is used,
polyclonal antibody preparations typically also contain antibodies that
do not bind to the chosen category of target cell and may contain antibodies that bind to
other categories. Additional antibodies are present because the antibody repertoire
of an animal is determined by the animal's infection history. Therefore, polyclonal
antibodies are preferably purified by affinity methods. Category-binding molecules
in a family might bind to some target cells in the category but not to others.
[0054] Another example of a family of category-binding molecules is a set of 80 category-specific
genomic DNA sequences that occur in all
E.
coli O157:H7 strains but that do not occur in members of other groups of bacteria. This
family of category-binding molecules can hybridize as a group to suitably prepared
E.
coli O157:H7 cells, but does not hybridize to other categories of cells. Families can
include different types of category-binding molecules. For example, a monoclonal antibody
that specifically binds to the O157 antigen and one that binds to the
intimin protein (a virulence factor) could also be included in the above family of category-binding
molecules. A family of category-binding molecules can comprise any number of category-binding
molecules (
i.
e., one or more).
[0055] By
non-overlapping families of category-binding molecules is meant families of category-binding molecules in
which each family binds specifically to one, and only one, category in a set of non-overlapping
categories. That is, a set of non-overlapping families of category-binding molecules
map to a congruent set of non-overlapping categories. For example, in a test that
scans the 4 USP objectionable organisms
E. coli,
Salmonella,
Pseudomonas spp., and
Staphylococcus aureus, there are four non-overlapping categories. Such a test might incorporate four different
non-cross-reacting polyclonal antibodies, each specific for one of the test categories.
Thus, the test comprises four non-overlapping families of category-binding molecules.
The non-overlapping families of category-binding molecules in a test are called an
ensemble of category-binding molecules.
[0056] By an
ensemble of category-binding molecules is meant a set of one or more non-overlapping families of category-binding
molecules that are combined in a mixture for a particular test. Tests that scan for
multiple non-overlapping categories of target cells comprise one family of category-binding
molecules per category. The entire set of category-binding molecules, that comprise
these families, is referred to as an ensemble.
[0057] By the
category-binding molecule complexity of an ensemble is meant the number of distinct category-binding molecules or moieties
in an ensemble. For example, if an ensemble of category-binding molecules consisted
of 234 oligonucleotide probes, the category-binding molecule complexity of the ensemble
would be 234.
[0058] By the
family complexity of an ensemble is meant the number of non-overlapping families of category-binding
molecules in an ensemble. The family complexity is the same as the minimum number
of target cells required to bind a category-binding molecule from each of the families
in an ensemble. The family complexity of a test corresponds to the categorical complexity
of a test -
i.
e., the number of distinct categories for which the sample is scanned. In general,
the family complexity also corresponds to the number of distinct signal signatures
used in a test.
[0059] By
signal element is meant a molecule or particle that directly generates a detectable signal. The
phrase "directly generates" refers to the fact that signal elements are the immediate
source or critical modulator of the detectable signal. Thus, if the signal is photons
that arise from a fluorophore, the fluorophore is the immediate source of the photons
and, therefore, is a signal element. If the signal is photons scattered by an RLS
particle, the RLS particle is a signal element. Alternatively, if the signal is the
light transmitted or scattered from a chromogenic precipitated product of the enzyme
horseradish peroxidase, the chromogenic product is the signal element.
[0060] A characteristic of a signal element is that such an element cannot be divided into
parts such that each part generates a signal that is comparable (in character, not
necessarily in intensity) to the whole. Thus, a 2 nM diameter quantum dot is a signal
element, as dividing it changes the character (emission spectrum) of the resulting
nanocrystals. A 5 µm particle impregnated with a fluorescent dye such as fluorescein,
is not a signaling element, since it could be divided into parts such that each part
has signaling characteristics comparable to the intact particle. The molecule fluorescein,
in contrast, is a signaling element. The detectable products of signal generating
enzymes (
e.
g., luciferase, alkaline phosphatase, horseradish peroxidase) are also considered signal
elements. Such signal elements (or their precursors when there is a chemical conversion
of a precursor to a signal element) may be diffusible substances, insoluble products,
and/or unstable intermediates. For example, the enzyme alkaline phosphatase converts
the chemiluminescent substrate CDP-Star (NEN; catalog number NEL-601) to an activated
product, which is a photon-emitting
signal element.
[0061] By
signaling moiety is meant a molecule, particle, or substance comprising or producing (in the case
of enzymes) one or more signal elements and that is or can be conjugated to a category-binding
molecule. The signaling moiety can be attached to the category-binding molecule either
covalently or non-covalently and either directly or indirectly (
e.
g., via one or more adaptor or "chemical linker" moieties). Examples of signaling moieties
include carboxylated quantum dots; a fluorophore such as Texas Red that is modified
for binding to a nucleic acid probe or an antibody probe; streptavidin-coated fluorescent
polystyrene particles (which can be conjugated to biotinylated category-specific binding
proteins); a rolling-circle replication product containing repeated nucleic acid sequences
each of which can hybridized to several oligonucleotides tailed with fluorescently
modified nucleotides and which contains a category-specific binding oligonucleotide
at the 5' end. A signaling moiety can comprise physically distinct elements. For example,
in some cases the signaling moiety is an enzyme (
e.
g., alkaline phosphatase) that is conjugated to a category-binding molecule (an antibody,
for example). Signal is generated when a substrate of alkaline phosphatase (
e.
g., CDP-Star, or BM purple from NEN and Roche, respectively) is converted to products
that are signal elements (
e.
g., an unstable intermediate that emits a photon, or a precipitable chromogenic product).
It is not unusual for the category-binding molecules, enzymatic signaling moieties,
and substrate to be applied to the reaction at distinct times.
[0062] By
signaling moiety complex is meant a physical cell that comprises more than one signaling moiety and more than
one category-binding molecule. The physical association of the signaling moieties
and category-binding molecules in a signaling moiety complex must be stable (
e.
g., the signaling moieties and category-binding molecules should have mean half-lives
of association with the complex of at least one day in PBS at 4°C). As an example
of a signaling moiety complex, consider a polystyrene microparticle that is coated
with thousands of molecules of two types: a target cell-specific antibody and alkaline
phosphatase. Such a signaling moiety complex binds to the target cell via the conjugated
antibody category-binding molecule. When incubated with a chromogenic alkaline phosphatase
substrate (the signal element; e.g., BM purple, Roche), a colored spot can be generated
that can be detected by eye. Alternatively, the same signaling moiety complex, when
incubated with either a chemiluminescent or a fluorescent alkaline phosphatase substrate,
generates either a chemiluminescent or fluorescent signal. Further examples of signaling
moiety complexes include: nanogold particles conjugated to fluorescein-labeled antibodies,
and latex particles conjugated to both oligonucleotide category-binding molecules
and acridinium esters that chemiluminesce upon addition of hydrogen peroxide.
[0063] By
signal character of a signal element or signal moiety is meant the aspect or aspects of a signal generated
by the signal element signaling moiety that is useful for distinguishing it from other
signal elements or signaling moieties. For example, the signal character of a signaling
moiety labeled with fluorescein and rhodamine is
fluorescence. The character of a radio transponder is
radio frequency. Examples of photonic signaling character are fluorescence, light scattering, phosphorescence,
reflectance, absorbance, chemiluminescence, and bioluminescence. All but the latter
two examples of photonic signaling character depend on external illumination (
e.
g., a white light source, a laser light source, or daylight). In contrast, chemiluminescence
and bioluminescence are signaling characters that are independent of external light
sources.
[0064] By the
class of a signal element or signaling moiety is meant the distinct quality of the signal
that is useful for distinguishing it from other signal elements or signaling moieties.
For example, a liposome that is labeled with red dye is distinguished from differently
colored liposomes. The color red is its class. For a micro-transmitter that broadcasts
a particular radio-frequency signal, the quality of the radio-frequency signal that
differentiates the micro-transmitter from other micro-transmitters constitutes the
signal element class.
[0065] By
signal signature is meant the distinctive signaling quality of the combination of signaling moieties
that bind to a category of target cells in a test. A target cell that is bound to
four types of antibodies, one of which is conjugated to a fluorescein molecule, and
three of which are conjugated with rhodamine molecules has a signal signature that
is described by the combined weighted absorbance and emission spectra of fluorescein
and rhodamine.
[0066] By
signal complexity of a test or an ensemble of labeled category-binding molecules is meant the number
of categories of target cells that can be distinctly labeled in the test or by binding
to the ensemble. Alternatively, the signal complexity is defined as the number of
distinct
signal signatures that would be expected to occur if a member of each category of target cell were
present. For some tests, the signal complexity of an ensemble of category-binding
molecules is the same as the number of categories for which the test scans. Other
tests, which scan for many categories, may only have a signal complexity of one.
[0067] By
selection force is meant a force that is used to capture, isolate, move, or sequester target cells.
Examples of selection forces include gravity, magnetism, electrical potential, centrifugal
force, centripetal force, buoyant density, and pressure. Target cells can be mobilized
by a selection force acting on the target cell alone. Alternatively, selection forces
can act specifically on target cells that are associated with selection moieties (see
definition below).
[0068] Examples of the application of selection forces to mobilize target cells include:
centrifugation of target cells; magnetic selection of target cells bound to magnetic
particles; gravitational sedimentation of target cells labeled with metallic particles;
and deposition of target cells on a porous membrane by vacuum filtration.
[0069] By
selection moiety is meant an atom, molecule, particle, or cell that can be conjugated to a category-binding
molecule and that confers on the category-binding molecule the ability to be selectively
captured, isolated, moved, or sequestered by a selection force. When a category-binding
molecule:selective moiety complex is specifically bound to a target cell, the target
cell can also generally be selectively captured, isolated, moved, or sequestered by
the selection force. Selective refers to the preferential conferring of susceptibility
to mobilization by the selection force on selection moieties and associated cells
over cells not associated with selection moieties.
[0070] Paramagnetic particles and ferritin are examples of selection moieties. A dense silica
particle that sinks in solution is another type of selection moiety. Such particles,
when coated with category-binding molecules and bound to a microbial target cell will
cause the target cell to sink in aqueous solution, thus enabling separation of the
bound target cell from other sample unbound constituents.
[0071] By
selective character is meant the aspect or aspects of a selection moiety that is useful for capturing,
selecting, or moving the selection moiety. For example, the selective character of
a paramagnetic particle is
magnetism. The selective character of a silica particle that rapidly sinks in aqueous solution
is
mass.
[0072] By a
roughly planar surface or substrate is meant a surface that can be aligned in parallel to an imaginary
plane such that when the distance is measured from points in any 1 mm x 1 mm square
on the surface to the closest points on the imaginary plane, the absolute value of
the mean distance is less than 50 micrometers.
[0073] By
detection surface is meant the surface of a roughly planar substrate onto which target cells are deposited.
In embodiments using photonic signaling character, if the detection surface is optically
transparent, detection can be effected via either face of the detection surface. If
the detection surface is opaque, detection is effected via the face of the detection
surface on which the target cells are deposited.
[0074] By
detection area is meant the area of the detection surface that is simultaneously sampled by a detection
device. For example, the section of a glass slide that is simultaneously imaged by
an optical device that includes a collection lens and a CCD chip might measure 0.8
cm x 0.5 cm. The detection area is then 0.4 cm
2.
[0075] By
detection zone is meant the volume in which replicating target cells can be detected by the detection
device. The detection zone has the same dimensions as the detection area but has a
depth corresponding to the depth in which the signal from replicating target cells
can be detected and identified. The depth of the detection zone is therefore dependent
on the threshold criteria used to score for positive signal. When optical detection
is used, the depth of the detection zone is dependent on the optical depth of field.
[0076] By the
longest dimension of a detection area is meant the line of maximum length that can be drawn between
two points on the perimeter of the detection area. For example, if the detection area
is a rectangle measuring 0.3 cm x 0.4 cm, the longest dimension of the detection area
is the diagonal, 0.5 cm. If the detection area is an ellipse with semi-major axis
of length 7 mm and semi-minor axis of length 2.5 mm, the longest dimension of the
detection area is 14 mm.
[0077] By
large area detection or large area imaging is meant a method for detecting microscopic target cells in which the detection area
(the area that is simultaneously analyzed by the detection device) is much larger
than the dimensions of the target cells or microcolonies. The detection area for large
area detection has at least one linear dimension that is ≥ 1 mm. In contrast, the
microscopic colonies are substantially smaller, typically measuring less than 50 µm
in at least two orthogonal dimensions. Examples of large area detection include imaging
a 9 mm diameter detection area with a CCD camera; imaging a 2 cm x 1 cm rectangle
by scanning with a linear array detector that has a long dimension of 1 cm; and imaging
a 4 cm x 4 cm filter using direct exposure on photographic film.
[0078] Some technologies scan samples for microcolonies but do not exploit large area detection.
Examples include solid phase laser microbeam scanning cytometry and microscopic examination
of multiple high power microscopic fields on a slide.
[0079] By c
onjugated or
stably associated is meant a physical association between two entities in which the mean half-life
of association is least one day in PBS at 4°C. Consider, for example, the complex
case of passive protein adsorption to polystyrene particles. There are several different
classes of adsorbed proteins. Some proteins are stably associated to the surface with
half-lives of many months. Other proteins, such as those that are loosely bound on
the outer layer of adsorbed protein, may not be stably associated with the particles
and can leach out within hours.
[0080] By
particle is meant a rigid matrix (i.e., with at least some characteristics of a solid), which
measures less than one millimeter along any axis. Particles can be doped with or conjugated
to signal elements. Particles are often referred to as particles or with terms that
reflect their dimensions or geometries. For example, the terms
nanosphere,
nanoparticle, or
nanobead are used to refer to particles that measures less than 1 micron along any given axis.
Similarly, the terms
microsphere, microparticle, or
microbead are used to refer to particles that measure less than one millimeter along any given
axis. Examples of particles include latex particles, polyacrylamide particles, magnetite
microparticles, ferrofluids (magnetic nanoparticles), quantum dots,
etc.
[0081] By
image intensifier or
image tube is meant a device that amplifies a photonic signal, as defined in the glossary
of Inoué. Shinya,
et al.,
Video microscopy: the fundamentals (Plenum Press, New York, 1997; p. 665): "A device coupled (by fiber optics or lenses)
to a video camera tube to increase sensitivity. The intensifier is a vacuum tube with
a photocathode on the front end that emits electrons according to the image focused
upon it, an electron lens and/or microchannel plate(s) that focuses the electrons
onto a phosphor at the back end, and a high voltage accelerator that increases the
energy of the electrons. Can be single or multiple stage." A variety of such image
intensifiers is described in detail in Chapter 8 of the same reference.
[0082] By
simultaneous detection in a section of the detection area is meant detection of the signal from a section
of a roughly planar detection surface in one step. Large area imaging of targets in
a detection area using a CCD chip, visual detection, or photodiode-based signal integration
are examples of simultaneous detection.
[0083] By
identification is meant determining the category or categories of which a target cell is a member.
[0084] By
sample is meant material that is scanned by the invention for the presence of target cells.
[0085] By
direct visual detection is meant visual detection without the aid of instrumentation other than wearable
corrective lenses.
[0086] By
photoelectric detector is meant a man-made device or instrument that transduces photonic signals into electric
signals. Examples of photoelectric detectors include CCD detectors, photomultiplier
tube detectors, and photodiode detectors, e.g., avalanche photodiodes.
[0087] By
encircled energy or ensquared energy is meant the percentage of photons from an infinitely small light source that are
captured on a pixel of a photodector array.
[0088] By
thermal radiation is meant black body radiation.
[0089] By cellular autofluorescence or
autofluorescence is meant the fluorescence exhibited by cells due to the fluorescence of natural intrinsic
cellular constituents, such as NADH and oxidized flavoproteins. Cells expressing fluorescence
due to recombinant fluorescent proteins such as green fluorescent protein are not
considered to be autofluorescent.
[0090] By
in situ replication is meant the replication of a target cell in place, so that the daughter cells remain
essentially co-localized with the progenitor target cell. For example, in
in vitro biological culturing of bacteria on nutrient agar plates, single dispersed bacteria
are deposited on a plate and incubated under conditions that permit bacterial replication.
A bacterium in a certain location replicates giving rise to progeny cells that also
replicate. All of the cells remain co-localized (essentially contiguous) with the
original cell, eventually giving rise to a visible colony on the plate. Where there
was formerly a single cell, there is now a colony of more than 10
7 cells.
[0091] By a
microcolony of target cells is meant a set of target cells that lie in close physical proximity
to each other, that lie on (or are anchored to) a surface, and that are the clonal
descendants via
in situ in vitro replication-based amplification of a single ancestral target cell. A microcolony
is generally too small to be visible by the naked eye (
e.
g., less than 50 microns in diameter).
[0092] Any type of dividing target cell can give rise to microcolonies in situations that
lead to physical co-localization of the clonal descendents of the target cells. For
example, microcolonies could contain animal or plant cells, fungi, or bacteria.
[0093] By
illuminating is meant irradiating with electromagnetic radiation. Electromagnetic radiation of
various wavelengths can be used to illuminate. It includes, for example, radiation
with wavelengths in the X-ray, UV, visible, or infrared regions of the spectrum. Note
that illuminating radiation is not necessarily in the visible range.
[0094] By signal elements or signaling moieties with
photonic signaling character is meant signal elements or signaling moieties that are detectable through the emission,
reflection, scattering, refraction, absorption, capture, or redirection of photons,
or any other modulation or combination of photon behavior. Some examples of signal
elements or signaling moieties that have photonic signaling character include: the
fluorophore Texas Red (fluorescent signaling character); CDP-Star (chemiluminescent
signaling character); luciferase (bioluminescent signaling character); resonance light
scattering particles (light scattering signaling character); BM purple (light absorption
or chromogenic signaling character); and up-converting phosphors (absorption of two
long wavelength photons and emission of one shorter wavelength photon).
[0095] By
'number' X 'solution name' is meant an aqueous solution comprising the constituents of
solution name at
number times the concentration of the solution (except for water). For example, 10X EE contains
10 mM EDTA/100 mM EPPS (EE, or 1X EE, contains 1 mM EDTA/10 mM EPPS).
[0096] EE is a solution that is 1 mM EDTA/10 mM EPPS. Before mixing them together, the conjugate
acids of both components are brought to pH 8.0 with NaOH
[0097] PB is 0.1 M sodium phosphate buffer pH 7.4.
[0098] PBS is a phosphate-buffered saline solution containing: 120 mM NaCl, 2.7 mM KCl and 10
mM phosphate buffer (sodium salt) pH 7.4.
[0099] PBS-B is 0.1% BSA (IgG Free; Sigma Cat. No. A-7638) in PBS.
[0100] PBS-T is 0.05% Triton X-100 (Sigma Cat. No. X-100) in PBS
[0101] PBS-TB is PBS/0.1%BSA/0.05% Triton X-100
[0102] PBT is PBS/0.1% BSA (IgG Free; Sigma Cat. No. A-7638)/.05% Tween-20 (Sigma Cat. No X-100)
[0103] LB is Luria Broth for growing bacteria and is made as described previously (Ausubel
1987,
supra).
[0104] SSC is 150 mM NaCI/15 mM Na
3 citrate adjusted to pH 7.0 with HCl.
[0105] EDAC is (1-Ethyl-3-(3-dimethylaminopropyl)) carbodiimide.
[0106] TSA is, Tryptic Soy Agar (Becton Dickinson/Difco; cat. num. 236950).
[0107] TSB is Bacto
™ Tryptic Soy Broth (Becton Dickinson cat. num. 211822).
[0108] AP is alkaline phosphatase.
[0109] BSA is Bovine Serum Albumin.
[0110] CCD is charged coupled device.
[0111] Cfu is Colony forming unit (a measure of bacterial concentration that corresponds to
the number of viable bacterial cells).
[0112] FITC is fluorescein isothiocyanate.
[0113] PNA is peptide nucleic acid.
[0114] Unless otherwise noted, microbiological strains described in the specifications are
obtained from the American Type Culture Collection (ATCC), Manassas, VA.
Brief Description of the Drawings
Figure 1. Traditional microbial culture requires many generations of cell division.
[0115] The long time-to-results of traditional microbial culture results from the time required
to generate enough microscopic target cells to be visible to the naked eye.
Figure 2. The concept for rapid detection of microbial growth by detecting microcolonies
[0116] The invention achieves rapid enumeration of growing cells by imaging microcolonies
containing fewer cells than do the macrocolonies that are detected by eye using the
traditional method. The invention is faster because fewer generations are required
than for the traditional method
Figure 3. A CCD imaging device for large area imaging
[0117] The CCD-based imager depicted in the figure was used to collect much of the data
described in the examples (see also Step 5 of Detailed Description section). In one
example, excitation light is provided by introducing light from a high intensity white
light source (1000 Watt Xenon arc lamp, Model A-6000, Photon Technology Incorporated,
Monmouth Junction, NJ) into a liquid light-guide (5 mm core diameter, Model 380, Photon
Technology Incorporated, Monmouth Junction, NJ). The liquid light-guide carries the
light to an excitation filter-wheel (BioPoint FW, Ludl Electronics, Hawthorne, NY)
and directs the filtered beam (typically 9 mm in diameter) onto the detection surface
containing the labeled target cells. The detection surface is the optically clear
bottom of a microtiter dish well. However, the same apparatus can detect labeled target
cells on various detection surfaces (
e.
g., microscope slides, coverslips, and tubes with flat, optically clear, bottoms).
The incident light strikes the detection surface inducing fluorescence in the signaling
moieties that are bound to target cells via category-binding molecules and that are
deposited on the optically clear surface. A portion of the emitted fluorescent light
is collected by a high-collection efficiency lens system and transmitted through an
emission filter-wheel (BioPoint FW, Ludl Electronics) to a CCD Camera (Orca II, Hamamatsu,
Bridgewater, NJ).
Figure 4. A CCD imaging system for non-magnified large area imaging
[0118] The figure shows a CCD imager with an angular illumination configuration in which
light is introduced onto the detection surface (shown here as the bottom of a well
of a microtiter plate) at an angle from the side of the collection optics. The angle
is chosen to optimize collection efficiency and to avoid obstruction of the incident
beam by the collection lens. The advantage of this configuration is that reflections
from the bottom surface of the sample holder are not collected by the collection lens
and therefore do not contribute to the fluorescence background noise.
Figure 5. Reagent-less detection of microcolonies using non-magnified large area imaging.
[0119] The figure diagrams a rapid method for enumerating bacterial growth without using
a labeling reagent. The intrinsic autofluorescence of target cells in microcolonies
is detected using CCD-based non-magnified large area imaging. Advantages of this reagent-less
approach include its simplicity, non-destructiveness, and broad applicability. Alternatively,
labeling reagents that bind to target cell-specific binding sites (
e.
g., fluorescent antibodies or nucleic acid probes) can be used for detecting microcolonies
containing target cells.
Figure 6. Detection and identification of bacterial microcolonies using non-magnified
large area imaging (Example 1)
[0120] The figure shows a rapid, simple, and sensitive method for detecting microcolonies
by imaging labeled microcolonies using CCD-based non-magnified large area imaging.
In this example, single cells were allowed to go through several replicative generations
in order to form microcolonies. The microcolonies were labeled with either Syber Green
I or a FITC-labeled antibody. In Figure 7 the upper row of panels shows the 0 hour
time point containing single cells. The lower row of panels shows microcolonies after
3 hours of incubation. There is a substantial increase in size and signal of the objects
detected by CCD imaging over time due to the increase in the number of cells at the
sites where the colony-forming cells were originally deposited.
Figure 7. Autofluorescence-based detection of bacterial microcolonies using non-magnified
large area imaging (Example 2)
[0121] The figure diagrams a rapid, simple, and sensitive method for detecting microcolonies
by imaging cellular autofluorescent signals using CCD-based non-magnified large area
imaging. Single dispersed cells were deposited on a filter, which was incubated on
growth medium for 5.25 hours at 37°C. Microcolonies (resulting from the clonal growth
of the single dispersed cells) generated substantial autofluorescent signal (left
panel) when compared to a filter on which no bacteria were deposited (right panel)
but that was otherwise prepared and imaged identically.
Figure 8. A simple method for validating a rapid reagent-less microbial enumeration
test using an internal comparison to the traditional culture method (Example 3)
[0122] The figure demonstrates a simple method for showing the equivalence of microcolony
enumeration to the traditional method. Using non-destructive detection of Microcolony
autofluorescence allows the microcolonies detected by the invention to be re-incubated
until they mature into the macrocolonies that are detected using traditional visible
colony counting. Note that the pattern of spots formed by the microcolonies (left
panel) matches the pattern formed by the visible colonies (right panel) indicating
the equivalence of the two methods.
Figure 9. Accuracy and limit of detection of autofluorescent microcolony detection
using non-magnified large area imaging (Example 4)
[0123] The figure shows the method used to measure the accuracy of the invention when the
samples contain extremely low levels of target cells. For each of the 101 filters,
the result obtained by scoring the autofluorescent microcolonies was the same as the
result obtained by the traditional method.
Figure 10. Determining the number of microbial cells in autofluorescent bacterial
microcolonies rapidly detected using reagent-less non-magnified imaging (Example 5)
[0124] The figure shows the signal generated from microcolonies of
E. coli using large area imaging from
Escherichia coli microcolonies (top panel). The three microcolonies imaged with high powered microscopy
in the bottom panels correspond to the three microcolonies imaged using the invention
in the upper panel. The number of bacteria in each microcolony is indicated below
each frame (45, 48 and 50 celis). The figure demonstrates that microcolonies containing
low numbers of
E.
coli cells can be detected using reagent-less non-magnified large area imaging.
Figure 11. CCD-based, non-magnified, large area imaging detection and identification
of bacterial microcolonies in an environmental water sample (Example 6)
[0125] The figure shows the analysis of bacterial growth by using the invention to detect
bacterial colonies in water from the Charles River. Bacterial cells were collected
onto mixed cellulose ester filters. The filters were placed onto an R2A agar plate,
and incubated for 74 hours at 32.5°C. At various time points the filters were imaged
using reflectance of white light and autofluorescence. Macrocolonies that were 0.55
mm or greater in diameter were identified and counted in the reflectance images. The
time points at which autofluorescent microcolonies that gave rise to a macrocolonies
could be detected was also determined. At various time points the percentage of the
74 hr macrocolonies that were detectable as autofluorescent microcolonies was plotted.
Figure 12. Correlation between CCD-based, non-magnified, large area imaging detection
of bacterial microcolonies and a classical pour plate culture method for enumerating
bacteria in a sample (Example 7)
[0126] The figure compares the enumeration of autofluorescent microcolonies obtained using
the invention and the traditional pour plate method of microbial culture.
Figure 13. Dynamic range and linearity of a reagent-less enumeration test (Example
8)
[0127] The figure shows the analysis of dynamic range and linearity by using the invention
to detect autofluorescent microcolonies.
Figure 14. Antimicrobial preservative effectiveness testing without sample dilutions
(Example 9)
[0128] The figure shows that comparable antimicrobial preservative effectiveness results
are obtained using invention and traditional methods. The comparison shows the potential
of the invention to eliminate most of the labor and expense of this test by obviating
the need to analyze hundreds of sample dilutions.
Figure 15. Autofluorescence-based detection a heat-stressed biological using non-magnified
large area imaging (Example 10)
[0129] The figure shows the correlation between enumeration of heat-stressed biological
indicator cells using the invention and the traditional pour plate method. The biological
indicator
G.
stearothermophilus was subjected to a variety of heat stress regimes. Microcolony autofluorescence was
measured using CCD-based large area imaging and visible macrocolonies were counted
visually. The results of the two methods are plotted against each other and show good
correlation. The invention, however, required substantially fewer dilutions than did
the traditional method.
Figure 16. Autofluorescence-based detection of bacterial microcolonies in ground beef
(Example 11)
[0130] The figure shows the detection times of autofluorescent microcolonies and macrocolonies
derived from microbes in ground beef. Tracking the appearance over time of microcolonies
that gave rise to the 48 hr macrocolonies showed that 100% of the macrocolonies were
detected by the invention at 16 hrs. This shows the potential of the invention to
reduce the time required to achieve results significantly compared to traditional
methods.
Figure 17. Magnetic selection followed by microcolony detection (Error! Reference source not found. and Example 12)
[0131] A scheme is shown for magnetic selection of target cells followed by
in situ growth and detection of microcolony autofluorescence using the invention.
Figure 18. Detection of bacteria in a complex sample with non-specific magnetic selection
followed by microcolony detection using non-magnified large area imaging (Example
12)
[0132] The figure shows results of an experiment in which
S.
aureus bacteria were magnetically captured from whole blood. The bacteria were selected
from a blood sample using magnetic particles coated with a mixture of broadly reactive
agents that bind bacteria. After filtration, plating, and incubation (6 hr), the autofluorescent
microcolonies were detected using non-magnified large area imaging. The filters were
allowed to incubate overnight. Afterwards, the filters were again imaged (images not
shown) and the position of six hour microcolonies were verified to have grown into
macrocolonies, eliminating the chance that the microcolonies would have been mistaken
for dust or other particulates.
Figure 19. Scheme for rapid antimicrobial susceptibility testing (Example 13)
[0133] The figure diagrams a rapid method for testing the sensitivity of a bacterial strain
to an antibiotic by detecting the appearance of microcolonies using CCD-based non-magnified
large area imaging. For the strain of bacteria shown, microcolonies cannot form when
the bacteria are grown in the presence of the antibiotic (right column) indicating
sensitivity to the antibiotic. Bacteria also do not grow without incubation under
growth conditions (left column). As expected, growth is detected when the strain is
incubated under growth conditions in the absence of the antibiotic (center column).
Figure 20. Rapid antimicrobial susceptibility testing (Example 13)
[0134] The figure shows the results of an antimicrobial susceptibility test that compares
the growth of bacterial strains (one sensitive to and one resistant to the antibiotic
tetracycline) as microcolonies on agar plates containing the antibiotic. Bacterial
cells from each strain were filtered onto a polycarbonate membrane, placed onto LB
agar plates containing tetracycline, and then incubated for three hours at 37°C (columns
labeled "3 hour"). Other filters prepared similarly were placed on LB agar plates
containing tetracycline for less than 5 minutes at room temperature (panel columns
labeled "0 hour"). The filters were fixed and stained with a nucleic acid stain. CCD
imaging of the membranes containing bacteria that were incubated for three hours (column
labeled: "3 hour CCD") detected microcolony growth on the membranes that contained
the resistant strain but not the sensitive strain. The growth of microcolonies on
the filters containing the resistant but not the sensitive strain was confirmed by
high power fluorescence microscopy (column labeled: "3 hour microscope"). As expected,
no microcolonies were detected on the CCD image of the filters that were not incubated
under growth conditions (column labeled: "0 hour CCD") and only single dispersed cells
were detected by high power fluorescence microscopy. Computer image analysis was used
to quantify the results of CCD imaging of the membranes (bar graph). The membrane
containing microcolonies formed by the resistant strain generated about 25-fold more
intensity than did the membrane containing the sensitive strain. The results of this
experiment show that detecting microcolonies using non-magnified large area imaging
is a rapid and sensitive method for antimicrobial susceptibility testing.
Figure 21. Rapid antimicrobial susceptibility testing using the disk diffusion method
and non-magnified large area imaging (Example 14)
[0135] The figure shows the results of an antimicrobial susceptibility disk diffusion test
comparing the growth of bacterial strains (one sensitive and one resistant). Autofluorescent
microcolonies, growing on or near the diffusion disk, were detectable after 5 hours
of growth, greatly reducing the time to detection from a standard overnight growth.
The left panel shows the Tet resistant strain growing close to the diffusion disk,
while the right panel shows the lack of growth of the Tet sensitive strain. The disk
diffusion plates were allowed to incubate overnight. The 24 hour zones of inhibition
were compared to the 5 hour zones. The 24 hour zone of inhibition was the same as
the 5 hour microcolony result indicating that the invention can yield faster but comparable
results compared to the traditional method.
Figure 22. Rapid Antimicrobial susceptibility testing using the E-test™ and non-magnified large area imaging (Example 15)
[0136] The figure shows the results of an antimicrobial susceptibility test comparing the
growth of bacterial strains (one sensitive and one resistant) using Etest
™ strips containing the antibiotic tetracycline. Bacterial cells were spread on TSA
plates. An E-test strip was added directly to the plates, which were incubated at
37°C. Autofluorescent microcolonies growing on or near the E-test™ strip were detectable
after 5 hours of growth. The left panel shows the resistant strain at 5 hours growing
near the 256 µg/ml segment of the strip. The right panel shows the sensitive strain
at 5 hours with a zone of inhibition near the 2 µg/ml segment of the strip. The E-Test™
plates were allowed to incubate overnight. The 24 hour zones of inhibition were comparable
to the 5 hour zones indicating that the more rapid results obtained with the invention
are comparable to the slower traditional method.
Detailed description of the invention
Overview of the invention
[0137] The invention rapidly and cost-effectively analyzes a minimally processed sample
for growing cells. Both the invention and traditional bacterial culture methods measure
cell growth by detecting the formation of bacterial "colonies" - clusters of associated
cells that arise from single cells via successive cell divisions (Figure 1). However,
the invention detects cell growth more quickly than traditional microbial culture,
because it detects microcolonies that appear at an earlier stage than the visually
observed macrocolonies detected by traditional microbial culture (Figure 2, Figure
8). By using the same method principles as microbial culture the invention can retain
the advantages of the traditional method while still improving time to results significantly.
[0138] To understand how the invention detects microcolonies it is helpful to examine a
specific embodiment and application. For example, consider a test for enumerating
the microbes in the water used to manufacture an injectible pharmaceutical. The microbes
in a sample of the water (100 ml) are concentrated and immobilized by passing the
liquid through a porous membrane. The membrane containing the microbes is placed on
nutrient growth medium in a disposable petri dish. The microbes are incubated at 32
°C to allow them to replicate and to form microcolonies. Light is directed at the
surface of the membrane causing cells in the microcolonies to autofluoresce. This
autofluorescent signal derives from biomolecules that are present in the cells (
e.
g., NADH and oxidized flavoproteins). The autofluorescing microcolonies are then imaged
electronically. Light originating from an individual microcolony strikes a pixel or
small cluster of adjacent pixels on a CCD array photodetector. The number of autofluorescing
microcolonies is immediately calculated by image processing software and reported
to the user. Note that the process is identical to traditional microbial culture,
except that the invention detects the results faster and automatically.
[0139] Because this embodiment of the invention is non-destructive (
i.
e., does not kill or injure the microbes), the detected microcolonies can be grown
into pure cultures. These pure cultures can be used for microbial identification -
and, for clinical samples, for determining antimicrobial resistance and susceptibility.
Non-destructive detection also makes it simple to validate the equivalence of the
method to traditional microbial enumeration. After detecting the microcolonies using
the invention, the petri dish can simply be re-incubated to allow the microcolonies
to growth for the length of time required to generate the visible macrocolonies detected
by traditional microbial culture detection. Comparing the number and location of microcolonies
detected by the invention to the visible colonies derived from further growth of the
microcolonies facilitates determining the equivalence of the invention and the traditional
method.
[0140] The invention can be used to construct tests using a number of formats, labeling
methods, category-binding molecules, and detection methods. However, the tests have
several key features in common. The steps and processes that are common to many embodiments
of the invention are described below.
The general configuration of applications of the invention includes the following
steps:
[0141]
Step 1: Formulating the test question, choosing the sample, categories of cells to
be detected, growth conditions, and signaling character
Step 2: Depositing the cellular targets in the detection area
Step 3: Allowing cellular replication to form microcolonies
Step 4: Optional labeling of microcolonies
Step 5: Enumerating the microcolonies
[0142] Formulating the question to be answered by the test is the first step in creating
a new application based on the invention. Some examples of important questions that
industrial and clinical microbiologists must address are listed in Table 3. Articulating
the test question generally defines the sample type that must be tested (
e.
g., ground beef, clinical urine sample, or a pharmaceutical finished product). The
sample type and volume are important parameters in choosing methods for depositing
the target cells in the detection area (see step 2). Articulating the test question
also defines the types, or categories, of cells that must be detected in the application
(
e.
g., aerobic bacteria, yeast or molds,
pseudomonas,
E. coli O157:H7, or an anonymous spinal fluid isolate).
Table 3.
Examples of questions answered by tests based on the invention |
• Do the numbers of bacteria in a urine sample indicate a urinary tract infection? |
• Does a patient's blood sample contain viable infectious microbes? |
• Which antibiotic is best for treating a particular patient with bacterial meningitis? |
• How many aerobic bacteria are present in 25g of meat? |
• Are there any cells of the foodborne pathogen E. coli 017:H7 in a sample of ground beef? |
• How many yeast and mold cells are present in an environmental air sample? |
• How many Pseudomonas cells are present in 10g of an over-the-counter pharmaceutical tablet? |
• Is the finished product batch of injectible drug sterile? |
• How many yeast cells are present in a production sample of beer? |
[0143] After defining the type of cells to be detected or enumerated, conditions are chosen
for fostering the growth of the cells in the detection area. Important parameters
for allowing cellular replication include: composition of the growth medium, presence
of selective reagents such as antibiotics, temperature, and the level of oxygen and
other gases. If possible, growth conditions are chosen that foster growth of the cells
to be detected but that are refractory to the growth of other types of cells. For
example, media for detecting yeast and molds often contain ingredients that inhibit
the growth of otherwise more rapidly growing bacterial microbes.
[0144] A method for generating detectable signal from the cells to be detected must also
be chosen. Choosing the signal depends on the type of cells in the microcolonies to
be detected, the types of other cells that might form microcolonies, and the type
of background expected in the sample. Consider a test for determining the total number
of aerobic bacteria in a finished product in pharmaceutical manufacturing; a wash
solution for contact lenses, for example. Because a broad spectrum of thousands of
environmental microbes could be present in such a sample, the signal generating method
must be very general. Some such methods rely on the intrinsic optical properties of
the microcolonies, such as microcolony autofluorescence, reflectance, or infrared
absorbance. Such methods allow rapid microcolony detection without using a reagent
- an important advantage of the invention. Reagent-less signal generation using, for
example, microcolony autofluorescence, substantially simplifies test methods, allows
aseptic sample processing, and enables rapid tests that use the same media and disposables
used in "gold standard" methods. Alternatively, microcolonies generated by the target
cells can be labeled using stains.
Using stains and specific probes to enumerate specific categories of target cells
[0145] Using stains or probes that bind to molecular constituents of target cells can be
used in applications that ask a range of diagnostics questions. Examples of stains
that can be used to detect a broad range of target cells (
e.g., all aerobic bacteria) include nucleic acid stains (
e.
g., propidium iodide or Syber Green (Molecular Probes)), and stains for enzyme activity
(
e.
g., fluorogenic esterase stains). To label narrower categories of target cells, labeled
probes that bind to target-specific molecular constituents can be used. For example,
a fluorescently labeled antibody that binds specifically to a molecule that only occurs
on the surface of the food pathogen
E. coli O157:H7 can be used to detect pathogenic microcolonies in a food sample.
[0146] Thus, to detect the presence of a category of target cells, the invention can use
molecules that bind specifically to category-specific molecular constituents. The
category-specific molecular constituents that occur on target cells are called
category-specific binding sites and the molecules that bind specifically to them are called
category-binding molecules. To detect the binding of category-binding molecules, a detectable label, or
signaling moiety is generally attached to the category-binding molecules. Note that category-specific
binding sites are a property of target cells that are potentially present in the sample
to be tested. In contrast, category-binding molecules are a reagent provided in a
diagnostic test kit.
[0147] An advantage of the invention is that a broad spectrum of category-binding molecules
can be used. This feature is important since different types of category-binding molecules
are used to ask different types of diagnostic questions (
e.
g., broad kingdom-level screening
vs. narrow subspecies-level identification). Classes of category-binding molecules (also
sometimes referred to as probes) comprise: nucleic acids (oligonucleotides, aptamers,
cloned sequences, genomic DNA, RNA, etc.); chemical variants related to nucleic acids,
such as peptide nucleic acids (PNA); antibodies; enzymes (which can bind target substrates);
non-enzymatic proteins such as avidin (which binds the target molecule biotin); molecules
that bind cellular constituents specifically (
e.
g., phalloidin which binds actin or biotin which binds avidin); dyes and stains, (e.g.,
propidium iodide, auramine-rhodamine, or SYTO 17); ligands (
e.
g., epidermal growth factor, which binds specifically to the epidermal growth factor
receptor); and polypeptide or nucleic acid binding reagents that have been selected
using
in vitro evolution techniques (
e.
g.,
Zhang et al., Nat. Biotech. 18: 71-74, 2000).
[0148] Category-binding molecules can incorporate other functional domains or modifications.
For example, category-binding molecules are often covalently or non-covalently associated
with signaling moieties (
i.
e., a labeling domain such as a fluorophore or a dyed microparticle) or selection moieties
(
e.
g., magnetic particles or solid surfaces). Alternatively, a category-binding molecule
may be linked to an adaptor moiety that, in turn, facilitates linkage to another functional
moiety. Sometimes the category-binding molecule has dual non-separable functions.
For example, propidium iodide, a nucleic acid stain, can be used as a category-binding
molecule (
e.
g., the category-specific binding site might be the cellular nucleic acid in a yeast)
while, at the same time, the bound dye functions as a signaling moiety (
i.
e., it can fluoresce when bound to the category-specific binding site). Tests based
on the invention can incorporate more than one class of category-binding molecule
(
e.
g., antibodies and nucleic acid stain, or antibodies and oligonucleotides).
[0149] The simplest tests incorporate a
single type of category-binding molecule to scan for a
single category of target cell. For example, a test for
M. tuberculosis might use a monoclonal antibody that binds specifically to a category-specific binding
site on the surface of
M. tuberculosis. In another example, when screening for urinary tract infections, the single category
is "all cells" - or, if human cells are lysed, "all non-human cells" - and the single
type of category-binding molecule could be a nucleic acid stain (
e.
g., propidium iodide).
[0150] A
family of category-binding molecules is a set of distinct category-binding molecules that
bind to members of the same category of target cell. For example, a polyclonal antibody
raised to Hepatitis C virus is a family of antibodies since it comprises multiple
category-binding molecules that bind specifically to the same category of target cell
- in this case HCV. Another example of a family of category-binding molecules is a
set of 80 category-specific genomic DNA sequences that occur in all
E.
coli O157:H7 strains but do not occur in members of other groups of bacteria. This family
of category-binding molecules can hybridize as a group to suitably prepared
E. coli O157:H7 cells but does not hybridize to other types of cells.
[0151] To detect multiple categories of target cells, a test includes one family of category-binding
molecules for each category. A set of families of category-binding molecules is called
an
ensemble of category-binding molecules. For example, tests for pneumonia or tests for drugs
of abuse, must distinguish numerous categories of target cells from each other. One
family of category-binding molecule is used for each category of target cell. For
a pneumonia test, an ensemble of antibodies that react to category-specific antigens
on the surface of microbes that cause pneumonia might be used. One family in this
category-binding molecule ensemble might comprise polyclonal antibodies from the immunoglobulin
fraction of antiserum raised in a rabbit host and directed against
Streptococcus pneumoniae. Another family could comprise a recombinant antibody or a monoclonal antibody directed
against a coat protein of adenovirus.
[0152] The number of distinct groups or categories of target cells tested for by an ensemble,
i.
e., the
categorical complexity, is reflected by the number of families of category-binding molecules in the ensemble.
The number of families in an ensemble can, in turn, be accurately defined by a quantity
called the "minimum categorical derivation" of an ensemble. The
family complexity is the minimum number of distinct target cells required to bind members from each
of the families of category-binding molecules in the test ensemble. For example, consider
an ensemble of category-specific antibodies used to simultaneously test a sputum sample
for the presence of
Mycobacterium tuberculosis,
Legionella spp, and
Coccidoides immitus. The family complexity of the ensemble would be three, since a minimum of three target
cells, one from each pathogen category, would be required to bind to members of each
family of category-binding molecules in the ensemble. The ability of the invention
to identify a broad spectrum of target cell categories in a single test is a consequence
of its ability to scan a sample using an ensemble of category-binding molecules that
has a large family complexity.
[0153] Category-binding molecules used in conjunction with the invention should be specific
in that they should bind under assay conditions to the desired target cell but not
to other types of target cells meant to be distinguished by the assay or to other
possible constituents of the sample or test. Thus, in a test for upper respiratory
bacterial infection, potential category-binding molecules are screened to eliminate
those that cross react with normal (commensal) microbial constituents of the upper
respiratory tract.
[0154] Representative methods for obtaining and characterizing category-binding molecules
are included in the examples below.
[0155] The invention's ability to analyze a sample for numerous disparate categories of
target cells simultaneously derives from the ability to differentiate the signals
derived from the different categories of target cells. The invention discriminates
between categories by labeling each category-specific family of category-binding molecules
with signaling moieties such that it has a unique
signal signature. The ability to generate and detect large numbers of distinct signal signatures (
i.
e., high
signal complexities) enables construction of tests that analyze for numerous categories of target cells
(
i.
e., highly multiplexed tests).
[0156] The invention can exploit various types of
signal character including: fluorescence, scattered light, light polarization, chemiluminescence,
and radioactivity. Examples of signaling moieties and detection schemes using various
signal characters appear below. There can be multiple signal classes within a signal
character. For example, if the signal character is fluorescence, various characteristic
emission spectra are included in the signal classes (
e.
g., red fluorescence, green fluorescence, and blue fluorescence). In another example,
consider red fluorescent microparticles that are dyed with different concentrations
of the same fluorophore. In this case, fluorescence is the signal character, but the
different intensities of the particles constitute the classes of signal character,
i.
e., fluorescence intensity is the quality of the signal character that differentiates
one group of particles from another.
[0157] A great variety of signaling moieties can be used in conjunction with the invention
as demonstrated in the examples below. Signaling moieties can include simple fluorophores,
up-regulated phosphors, naturally fluorescent proteins (such as green fluorescent
protein and its relatives), dyes, enzyme:substrate systems (generating color changes
or chemiluminescence), fluorescent microparticles, light scattering particles, magnetic
particles, or radio transmitting microdevices.
[0158] Attaining a high
signal complexity is key to developing certain tests that scan for numerous types of target cells (
i.
e., tests with high
categorical complexity).
Achieving high signal complexity
[0159] The number of distinguishable labels (or signaling moieties) in a mixture is called
the
signal complexity. For highly multiplexed tests, it is sometimes advantageous to use signaling moieties
with high signal complexity. Three general approaches that can be used with this invention
to generate high signal complexity are: (
1) distinct labeling, (
2) combinatorial labeling, and (
3) ratio labeling.
- 1. For distinct labeling, probes in different probe families are tagged with a single
signaling moiety that can be readily detected in the presence of all other signaling
moieties in the experiment. Thus far, it has been difficult to achieve distinct labeling
at high signal complexities. This difficulty is present because most labeling methods
use optical signals (e.g., chromogenic, fluorescent, chemiluminescent) or radioactive labeling, and because
of the spectral bandwidth of optical signals and the limited range of signals detectable
by current instruments, the resolvable signal complexity using optical signals is
rather small. For example, the resolution of dozens of fluorophores with distinct
emission spectra is currently impossible because of spectral overlap.
- 2. Another way to achieve the high signal complexity used in the invention is to apply
a combinatorial labeling approach. Combinatorial labeling is a technique for achieving
high signal complexity using a relatively small number of distinct signaling moieties.
With this approach, distinct combinations of signaling moieties are bound to different targets. Currently, fluorophores are
a favored class of signal moiety for molecular diagnostics. However, given the complications
involved in analyzing multiple distinct fluorophores (arising in large part from overlap
of the excitation and emission spectra), it is only currently practical to incorporate
about seven or fewer conventional fluorophores. However, used in combination, seven
fluorophores can be used to generate 127 distinct signals (N fluorophores generate
2N - 1 combinations). High signal complexity can also be achieved via combinatorial
labeling using other types of signaling moieties. For example, particles impregnated
with different dyes, particles that fall into different discrete size classes, or
transponders emitting distinct radio signals could be used with this approach. Combinatorial
labeling using fluorophores has recently been applied with success for human karyotyping
(Speicher et al 1996, supra; Schröck et al 1996, supra). Instrumentation and software for analysis of combinatorial labeling experiments
is commercially available.
- 3. High signal complexity can also be obtained using the ratio labeling approach (Fulton,
et al 1997, supra). In ratio labeling, as in combinatorial labeling, many distinct types of signaling
moieties are generated using a relatively small number of distinct signaling elements.
However, in contrast to combinatorial labeling, the signaling moieties in ratio labeling
are distinguished by the ratio of the signaling elements. For example, two fluorophores,
X and Y, with different excitation/emission characteristics can be used to dye polystyrene
particles. Different relative concentrations of the fluorophores ([X], [Y]) are applied
to different sets of particles. For example, eight different concentrations of X and
eight different concentrations of Y can be used to dye particles in all combinations
(X1Y1, X1Y2, X8Y7, X8Y8) resulting in 64 classes of distinguishable particles. Ratio labeling simplifies
instrumentation, as only a small number of signal types need be used. Signal elements,
other than fluorophores and including non-optical signal elements, can also be used
to generate high signal complexities using a ratio labeling approach.
Generating strong signals to facilitate the detection microcolonies
[0160] The level of signal intensity needed is, of course, dependent on the type of signal
character and the detection method/instrumentation (see below).
[0161] Various approaches for labeling category-binding molecules can be used to achieve
the required sensitivity. One method for optimizing the signal strength is to label
target molecules with highly fluorescent signaling moieties. For example, quantum
dots, fluorescently dyed nanospheres, and polymerized fluorophore molecules generate
strong fluorescent signals. Incorporating numerous signal elements can increase the
fluorescence intensity of a signaling moiety. For example, fluorescent nanospheres
(∼20 nm in diameter; Molecular Probes) can generate a signal equivalent to about 180
fluorescein molecules. Fluorescently dyed polystyrene microparticles (
e.
g., 1 µm in diameter) can incorporate millions of fluorophore signaling elements. A
method for incorporating multiple fluorophores in a signal moiety associates with
a nucleic acid category-binding molecule is to incorporate fluorophore-dNTP conjugates
during PCR amplification of a cloned category-specific sequence. Alternative methods
for incorporating multiple fluorophores into nucleic acid category-binding molecules
include approaches using: dendrimers, branched DNA, or rolling circle templates bound
to multiple signal moieties, or tailing with numerous polymerized fluorophore labeled
nucleotides. Conjugating category-binding molecules to multiple signaling moieties
also increases signal intensity. For example, signal amplification can also be achieved
by conjugating large numbers of signaling enzymes (
e.
g., alkaline phosphatase or horseradish peroxidase) to a nanoparticle.
[0162] Another approach to obtain strong signals is to bind numerous labeled category-binding
molecules to each cell. This binding can be achieved by various means including: using
multiple category-binding molecules (recognizing multiple category-specific binding
sites in the same target cell) or by choosing category-binding molecules that bind
to target molecules that are highly represented in a target cell. For example, a labeled
microbe-specific polyclonal antibody can achieve high signal intensities by binding
to numerous distinct epitopes on a microbial target cell. The strategy of choosing
category-specific binding sites that are present in large numbers in each target cell
has been frequently used previously. Examples of this strategy include the use of
nucleic acid probes for ribosomal RNA (which depending on the target organism and
cell type can be present in thousands of copies per cell). Similarly, some antigenic
target molecules are present in hundreds or thousands of copies in each cell of a
target organism. The invention can exploit both of these strategies. As another example,
the large number of category-specific binding sites present in a bacterium yield strong
signal intensity when using the nucleic acid-binding fluorescent dye Syber Green I
as the category-binding molecule/signaling moiety.
[0163] Binding numerous signal moieties to a target cell not only increases signal strength,
but it endows the invention with robustness since the chances are small of observing
numerous clusters of a large number of signaling moieties with the expected composite
signal signature in the absence of the target cell.
Conjugating signaling moieties to category-binding molecules
[0164] The invention can incorporate numerous types of signaling moieties which can be directly
conjugated to category-binding molecules using various methods which are known by
those familiar with the art (see, for example,
Hermanson, G., Bioconjugate Techniques (Academic Press, 1996) and specific examples below). For example, antibody or oligonucleotide category-binding
molecules can be directly conjugated to a fluorophore or a quantum dot signaling moiety.
Alternatively, antibodies or oligonucleotide category-binding molecules can be used
to coat fluorescent microparticle-based or light-scattering nanoparticle-based signaling
moieties. Signaling moieties can be indirectly conjugated to category-binding molecules.
For example, avidin can be directly conjugated to multiple signal elements to constitute
a signaling moiety. The labeled avidin molecule can then be bound to a biotinylated
category-specific antibody. Signaling moieties can be conjugated to the category-binding
molecules before, during, or after the binding steps. For example, in one embodiment
of the invention, digoxygenin-labeled nucleic acid probes are used as the category-binding
molecules. After binding the category-binding molecules to the category-specific binding
sites in the target cells in the sample, the unbound probes are washed away. Anti-digoxygenin
antibody:alkaline-phosphatase conjugates (the signaling moieties) are then conjugated
to the bound digoxygenin-labeled probes. An alkaline-phosphatase substrate (
e.
g., the chemiluminescent substrate CDP-Star; NEN)) is then added to the bound alkaline-phosphatase
to generate the signal.
Step 2: Depositing the cellular targets in the detection area
[0165] Depositing the target cells in the sample in the detection zone is generally the
next step in applications based on the invention. Essentially planar detection zones
are often used, in part, because optical imaging systems can efficiently collect light
from thin detection zones (
i.
e., optical systems with a small depth of field), for example, when microcolonies are
grown on the surface of the nutrient agar or on membranes lying on the surface of
nutrient agar plates. In these cases, the depth of the detection zone can be negligible
compared to the lateral dimensions of the detection zone. This step can also be used
to deposit certain target cells selectively, to remove substances that might inhibit
cell growth, or to contact target cells with labeling reagents.
[0166] Using membrane filtration to deposit cells on a roughly planar membrane detection
surface has several advantages. The ability to collect small numbers of target cells
from large sample volumes is one important advantage of using membrane filtration.
For example, a single bacterial cell in 1 liter of water can be quickly and efficiently
deposited on the surface of standard filtration membranes. Water can pass freely through
the membranes but cells can not, because of the size of the membrane's pores. The
water sample is poured into a container the base of which formed a membrane and then
a vacuum is applied to the bottom surface of the membrane. Water is drawn through
the membrane while cells are retained on the membrane surface. The membrane can be
optionally washed with liquid to efficiently remove substances such as growth inhibitors
or to expose cells to labeling reagents. The membrane can then be placed on growth
media.
[0167] Other methods for depositing the target cells on a surface include centrifugation,
gravitational settling, magnetic selection, or binding to surface bound category-binding
molecules (
e.
g., capture antibodies). In some cases (
e.
g., magnetic separation) a distinct moiety, the selection moiety is used. Magnetic
microparticles coated with category-specific antibodies are an example of a selection
moiety. After target cells are allowed to bind to the antibody-coated magnetic particles,
a magnetic field is applied to deposit the magnetically labeled cells on the detection
surface. Similarly, dense microparticles coated with target-specific antibodies can
be used as selection moieties. In this case, the labeled cells are brought to the
detection surface by the action of gravity on the dense particles.
Step 3: Allowing cellular replication to form microcolonies
[0168] In this step, target cells form microcolonies by dividing in place in the detection
zone. Microcolony growth is supported by exposing cells to growth medium containing
nutrients and incubating them under conditions that foster cell growth and division
(these parameters are selected in Step 1 above). In a typical embodiment, cells are
deposited on a porous membrane filter. The filter is placed on the surface of solidified
nutrient agar growth medium in a petri dish, which is then covered and placed in an
incubator set at the appropriate temperature. This method is currently used widely
to support colony growth using traditional microbial culture because nutrients can
diffuse freely through the membrane without causing movement of daughter cells from
the progenitor cell. Alternatively, microcolonies can be grown directly on the surface
of nutrient agar medium or the equivalent.
[0169] Selection for specific growth of the target cells can occur in the microcolony growth
step. For example, a test might be designed to detect anaerobic bacteria in a sample
(such a test is generally required for injectible pharmaceutical finished products,
for example). In this case, the growth step could be carried out under an anaerobic
atmosphere in a bell jar. Selective growth media can also be used to achieve selective
microbial growth at this step. For detecting bacterial resistance to antibiotics,
for example, cells are generally incubated in the presence of various antibiotics
at several concentrations. Resistance to a certain concentration of antibiotic is
inferred if a bacterial strain grows comparably in the presence and absence of antibiotic
at that concentration.
[0170] The invention can detect various colony morphologies. Many types of growing cells
form simple discrete dome-shaped colonies on common substrates (nutrient agar media
and membranes). Others form irregularly shaped colonies or filamentous colonies. Furthermore,
colony morphology can depend on growth conditions (
e.
g., nutrients, temperature, and substrate). Some types of cells are mobile and do not
form discrete colonies at all. If it is important to detect the growth of such organisms
motility inhibitors can be added to the medium. Thus, growth conditions should be
chosen and control experiments carried out to insure that target cells form detectable
microcolonies. If necessary, growth conditions can be modified or multiple conditions
may be used in parallel tests.
Step 4: Optional labeling of microcolonies
[0171] In this optional step, category-binding molecules and associated signaling moieties
(also called the probes, labels, or stains) are brought into contact with target cells
in the sample under conditions that facilitate specific binding. For example, an ensemble
of category-specific nucleic acid sequences is hybridized to complementary target
sequences in the sample in this step. Similarly, category-specific antigens in the
sample are allowed to bind to the corresponding category-specific antibodies.
[0172] There are several possible physical configurations for the binding step and binding
can be carried out at various points in the testing procedure. For example, target
cells can be labeled in a liquid sample before depositing the target cells in the
detection zone. Unbound probes can then be effectively removed during the depositing
step or by washing. A disadvantage of this approach is that the signal generally does
not increase with microbial growth. Stronger signals are generally obtained by labeling
microcolonies during or after microbial growth. The labeling reagent can be added
to the nutrient media so that the microbes are continuously exposed to the reagent
during growth. Alternatively, microcolonies can be exposed to the probes after growth.
For example, microcolonies on a membrane can generally be fixed and the relevant category-specific
binding sites exposed by drying, heating, and/or exposure to chemicals (
e.
g., NaOH or chloroform vapor). Labeling can then be effected by overlaying the microcolonies
with the labeling reagent or by placing the membrane on a pad that has been saturated
with the reagent. Generally, a washing step is used to remove unbound reagent. The
concentration of the category-binding molecules is optimized to achieve rapid binding
kinetics. The chosen conditions for selecting for specific binding depend on the characteristics
of the category-binding molecules and their interactions with target molecules. Specific
conditions and procedures are described in the examples below.
Step 5: Enumerating the microcolonies
[0173] Enumerating the target cells in the sample occurs in the final step of testing applications
based on the invention. The enumeration step itself comprises the steps of imaging,
image analysis, and report generation.
[0174] The invention can detect
microscopic colonies with
no magnification. Low magnification imaging facilitates the imaging of a large area which, in turn,
facilitates scanning large samples. Some embodiments of the invention detect microscopic
colonies without magnification, in part, by using high efficiency optics to direct
photons emitted by the microcolony into a small number of pixels of photodetector
arrays.
[0175] The imaging method used depends on the type of signal generation chosen in step 1.
For example, the imaging process is different depending on the optical property or
signaling character that is used for signal generation. For some signal characters
(
e.
g., reflectance, fluorescence, light scattering, absorbance), the complexes in the
detection zone must be illuminated by a light source. For others (
e.
g., chemiluminescence, thermal radiation), illumination is not required. Various detectors
can be used including electronic photodetectors, film, and direct visualization.
[0176] Detection of individual microcolonies is naturally quantitative and ultra-sensitive.
Quantification can be accomplished manually by counting individual cells in a photographic
or digital image or by using automated image analysis of digitized images. Integrating
signal intensity over the sample can also be used to quantify the target cells. Signal
integration is particularly useful with samples containing high concentrations of
target cells. In these cases, resolving coincident signals may not always be possible.
[0177] Decoding the signatures of labeled probe families allows identification of numerous
categories of target cells. An important goal of this step is to identify the category
of target cells in the sample by determining the signature of labeled category-binding
molecules that have adhered to the sample.
[0178] The CCD camera-based imager, shown in Figure 3, is a useful device for large area
imaging using when fluorescence is used as the signal character. This device was used
to collect the data for many of the examples below. Excitation light may be provided
by introducing light from a high intensity white light source (1000 W Xenon arc lamp,
Model A-6000, Photon Technology Incorporated, Monmouth Junction, NJ) into a liquid
light-guide (5 mm core diameter, Model 380, Photon Technology Incorporated, Monmouth
Junction, NJ). The liquid light-guide carries the light to an excitation filter-wheel
(BioPoint FW, Ludl Electronics, Hawthorne, NY) and directs the filtered beam (
e.
g., 9 mm or more in diameter) onto the detection surface containing the microcolonies.
The apparatus can detect microcolonies in various configurations (
e.
g., on the surfaces of nutrient agar, microscope slides, coverslips, or tubes or wells
with flat, optically clear, bottoms; or immobilized in nutrient agar or other substances).
The incident light strikes the detection surface inducing fluorescence in the target
cells. A portion of the emitted fluorescent light is collected by a high-collection
efficiency lens system and transmitted through an emission filter-wheel (BioPoint
FW, Ludl Electronics) to a CCD Camera (Orca II, Hamamatsu, Bridgewater, NJ). The design
and construction of the optical train is based on principles and practices known to
workers familiar with the art.
[0179] The invention can also incorporate other types of photodetectors and other configurations.
The sensitivity of the imaging system can be increased by choosing a more sensitive
camera (e.g., a camera cooled to a lower temperature, or a camera that uses a back-thinned
chip). Alternatively, the detection sensitivity and resolution can be increased by
implementing a line scanning system (e.g., BT Image Array; Hamamatsu). For line scanning,
a linear CCD or photodiode array (e.g., 1 x 500 or 1 x 1000 pixels) is used to capture
the image. The resolution in one dimension corresponds to the number of array elements,
while the second dimension is generally captured by moving the sample slide perpendicularly
under the linear array. Since there are fewer elements, similar sensitivity linear
arrays are typically less expensive than area format CCD cameras.
[0180] The instrument diagrammed in Figure 3 facilitates signal measurement from multiple
samples by using an X-Y positioning Stage (BioPoint XY, Ludl Electronics) to move
the sample vessel (e.g., microtiter plate) over the excitation and collection optics.
Image-Pro and Image-Pro add-ins control all instrument components and image acquisition.
Filter wheels are managed with the ScopePro add-in (Media Cybernetics, Baltimore MD),
and the StagePro add-in (Media Cybernetics, Baltimore MD) handles stage positioning,
while the camera control is via the Hamamatsu Orca II driver (Hamamatsu, Bridgewater,
NJ). Image-Pro Plus is also used for Image-Processing and analysis as described below.
[0181] Embodiments of the invention using white light illumination utilize spectral filters
to provide an optimal excitation peak for each of the fluorophores. The white light
spectrum is large, allowing a wide variety of fluorophores to be selected to eliminate
emission spectrum overlaps. Typically spot sizes achievable with white light illuminators,
e.g., 2 mm to 5 mm, are appropriate for large area imaging. Since filter changes are
relatively simple, and can be automated, white light systems are very adaptable, allowing
the same apparatus to be used for tests that use distinct sets of fluorophores.
[0182] The collection efficiency of the system shown in Figure 3 is maximized by incorporating
a custom designed collection optic consisting of two components: a collection objective
and a focusing element. The collection objective has high collection efficiency (≥
f#1.2) and outputs a relatively collimated beam. The focusing lens captures the light
output from the collection objective and focuses it onto the detection surface of
the CCD. The optics are designed in two parts to allow a filter wheel to be inserted
in the path of the collection lens. For certain embodiments of the invention,
e.
g. for some embodiments that do not require filter changes, it may be desirable to
include a tapered optical fiber bundle for achieving high collection efficiency. The
fiberoptic bundle contains fibers that collect light proximally to the sample and
channel the light directly to a CCD chip. Alternatively, the invention can detect
signals very sensitively using direct proximal detection in which the sample is applied
directly or in close proximity to the CCD chip (for highest sensitivity to the back
of a back-thinned CCD chip).
[0183] In addition to the white-light, multi-spectral system described above, we have also
developed a simpler single-color fluorescence imaging system for non-magnified large
area imaging. In the system shown in Figure 4, excitation light is provided by a 532nm
Frequency-Doubled Diode Laser (50 mW, Model # BWT-50E, B&W Tek, Newark, DE). Since
this detection uses a single color, filter wheels are not necessary. A single excitation
filter removes harmonics from the laser output (Model HQ532/10x, Chroma Technology,
Brattleboro, VT) and a single emission filter (Model HQ620/60m, Chroma Technology,
Brattleboro, VT) allows only specific fluorescent signals to pass to the CCD camera.
The systems also use a less-expensive CCD camera (Model KX-2E, Apogee CCD, Auburn,
CA) than the one described previously, to capture images. The instrument can easily
be adapted to multicolor analysis by incorporating multiple lasers and filter sets.
[0184] The CCD cameras incorporated in the invention are generally cooled to a temperature
between -5°C and -50°C, sufficient for integration times from ten seconds to about
two minutes (depending on the camera sensitivity) with minimal camera noise build-up.
Longer integration times generally give higher sensitivity by allowing the collection
of the photons emitted from the fluorophores for an extended period. Long integration
times are inappropriate for line scanning; however, there are back-thinned linear
arrays available that have very high quantum efficiencies, increasing sensitivity.
[0185] The invention can also use interferometer-based spectral imaging for the detection
and decoding of signals (Schrock, E., 1997,
supra). Using this technique, light emitted or scattered by signaling moieties is split
into two paths, passed thorough prisms (so that different wavelengths travel different
distances), and allowed to recombine to create an interference pattern. Fourier analysis
of the interference pattern generates a spectrograph for each point in the image.
[0186] Alternatively, photographic film can be used to record images of the target cells
inexpensively in a sample. When the signaling character is chemiluminescence, this
approach is most easily implemented. Images collected on film can be digitized in
commercial scanners for data storage and for digital image analysis.
[0187] For embodiments of the invention that generate digital images, computer software
identifies and quantifies the target microcolonies. For a typical assay in which different
classes of fluorescent signaling moieties are used, the software superimposes the
appropriate fluorophore-specific images, identifies the target cells by determining
which signature or combination of signals is emitted by each target microcolony, and
enumerates each category of target microcolony that is present in the sample. The
software may also: (1) correct for illumination non-uniformity; (2) correct for fluorescence
cross-talk through a deconvolution matrix; (3) align images using registration marks
imprinted on the substrate; (4) compare images from different time points; (5) apply
algorithms for discerning growing microcolonies from non-growing objects; (6) assign
an ID code to each imaged microcolony in the sample based on comparison to a look
up table; (7) record the imaged sample bar code for sample identification; and (8)
automatically save the output data, images, and bar code to a database that can be
queried, e.g., via a web browser interface. Commercially available image analysis
packages can be used to provide these functions. Software packages for multicolor
image analysis can be used (e.g., Image-Pro, Media Cybernetics; MetaMorph, Universal
Imaging; MatLab; The MathWorks).
[0188] It is useful to outline here the software packages and methods that were used to
analyze the fluorescence data collected in many of the examples that follow. The detection
surface was imaged to determine the number of fluorescent objects and/or the total
fluorescent signal. The fluorescence was captured from the membrane by a CCD camera
and stored as a TIFF (Tagged Image File Format) image file that contained records
of pixel locations and intensities. Three approaches were used to quantify the assay
results. The total integrated signal of the imaged detection zone was determined by
summing the fluorescent signal from all of the pixels. The integrated signal from
the sample was compared to that of negative controls. Measuring the total integrated
signal is especially useful for samples containing numerous target cells. A second
approach was to count the fluorescent objects in the detection zone. A third approach
was to integrate the intensity of all of the pixels contained within the fluorescent
objects (as opposed to summing the intensity of all of the pixels in the image). All
image analysis was performed using Image-Pro v 4.0 (Media Cybernetics, Silver Springs,
MD).
[0189] Obtaining the total integrated signal was achieved by initially defining an area
on the membrane (the area of interest). Image-Pro allows the area of interest to be
converted into a single object and other Image-Pro tools permit the total signal of
the pixels represented in this object to be summed. A similar image from a membrane
onto which no target cells were added was then analyzed in the same way and used as
a negative control. The negative control values were subtracted from the values of
target containing samples. This subtraction removed both assay and electronic noise.
[0190] The second and third quantification methods used Image-Pro's object-finding utility.
This utility joins contiguous pixels that have a value (signal) above an automatic
or user-defmed threshold. This establishes a contour line around the perimeter of
the object. The perimeter pixels and those inside are defined as the object, and summing
these pixel values results in the object integration value. The analysis software
was then used to count all the objects in an area of interest that represents the
bottom of the sample container and, in addition, could be used to calculate the integrated
signal intensity of all objects found.
[0191] Using the IPP Image-Pro macro language, the above utilities can be automated to allow
batch processing of several images at one time. In addition, the data can be manipulated
with other user-defined IPP scripts. For example, objects below or above a certain
size (area) or intensity can be included or excluded, which can be a useful tool for
dust exclusion. Other important parameters for image analysis that determine object
definition (e.g., acceptance and rejection criteria) vary by application and should
be optimized accordingly.
[0192] Various aspects of the invention can be automated including linking the steps outlined
above. Consider an application for analyzing liquid samples such has pharmaceutical
water for injection or a clinical urine sample. The automated system, starting with
the sample in a collection beaker, could collect the target cells onto a membrane
by filtration, place it on growth media, incubate the target cells under growth conditions,
image the membrane at regular intervals, and report the results. Alternatively, individual
functions of the invention can be automated. For example, modules for automatically
loading and unloading petri dishes (or alternative disposables used for growing microbes)
into the imaging instrument and for automatic focusing can be incorporated into the
system.
[0193] Examples. The examples below provide technical details for implementing various embodiments
of the invention for use in conjunction with a range of applications are not intended
to be limiting.
Example 1. Detection and identification of bacterial microcolonies using non-magnified
large area imaging
[0194] Background and objectives: Detection of microbial growth is at the core of both clinical microbiology (e.g.,
bacterial identification and antimicrobial susceptibility testing) and industrial
microbiology (e.g., mandated sterility testing), but the commonly used methods are
slow. The consequent delays in analysis cause needless death and suffering in clinical
situations and exact a large financial cost in industry.
[0195] Using non-magnified large area imaging to detect individual microcolonies exploits
the advantages of microbial culture while avoiding the substantial disadvantages of
traditional and emerging methods. Advantages of
in situ replication analysis using the invention are: speed; ease of multiplexing (scanning
for more than one microbe); and the ability to detect and identify without sacrificing
microcolony viability (essential for efficient antimicrobial susceptibility testing).
[0196] Experimental objective. The example demonstrates the invention's ability to detect
in situ replication of bacterial microcolonies. The principle of the method is diagrammed
in Figure 7. Bacteria are deposited on a filter and allowed to replicate
in situ. The resulting microcolonies were labeled in two ways: with the nucleic acid stain
Syber Green I and by binding to group-specific antibodies labeled with FITC. The labeled
microcolonies were then detected using CCD-based non-magnified large area imaging.
[0197] Experimental methods.
E. coli MG1655 cells were grown overnight in LB medium to a density of approximately 10
9 cells/ml. The approximate number of cells was determined by counting dilutions of
the overnight culture in a hemocytometer. The overnight culture was then diluted to
achieve about 10
3 cells/ml. One milliliter of the dilution was deposited on a black polycarbonate filter
(Osmonics; cat. num. K02BP04700) using a vacuum filtration device and a plastic funnel
cup (Millipore Microfil V User Guide, PF07114, Rev A 3/00). Sixteen separate filters
with ∼1000 cells were prepared in this manner, four filters for each of four time
points (0, 1.5, 3 and 24 hours). After filtration, each filter was placed on a separate
agar plate containing LB growth medium, which was pre-warmed to 37°C, and placed in
a 37°C incubator. Periodically (0, 1.5, 3, 24 hours) four filters were removed from
the incubator. Two of these filters were fixed in 3.0% formaldehyde for 10 minutes,
by adding the filter bacteria side up on top of a 500 µl spot of formaldehyde which
was spotted on a piece of Parafilm™. After fixation the filters were put on an absorbent
pad to soak up the excess formaldehyde. Next a 10X solution of Syber Green I (200
µl, Molecular Probes) was added on top of the filter. The cells were allowed to stain
for 10 minutes. The other two filters not used in the nucleic acid staining were blocked
with PBS-B for 15 min and then FITC labeled anti-E. coli antibodies (Fitzgerald) were
added to the filters. After 30 minutes of incubation, the filters were placed on an
absorbent pad to soak up any residual liquid. All membranes were then imaged by placing
the filter on a CCD-base imager (described in Step 5 of Detailed description section
and shown in Figure 3) so that the bacteria were facing the illumination source and
CCD chip.
[0198] Results. In this example single cells were allowed to go through several replicative generations
in order to from microcolonies. The microcolonies were labeled with either Syber Green
I or a FITC-labeled antibody. In Figure 6 the upper row of panels shows the 0 hour
time point containing single cells. The lower row of panels shows microcolonies after
3 hours of incubation. There was a substantial increase in size and signal of the
objects detected by CCD imaging over time due to the increase in the number of cells
at the sites where the colony-forming cells were originally deposited. The detection
of growth is central to medical and industrial microbiological practice.
[0199] This example shows that the invention can dramatically decrease the time required
for detection of microbial growth.
Example 2. Autofluorescence-based detection of bacterial microcolonies using non-magnified
large area imaging
[0200] Background and objectives: The importance of methods that detect microbial growth and the limitations of current
methods are discussed in the Background section. This example demonstrates a very
simple yet powerful method based on the present invention that rapidly detects the
growth of bacterial microcolonies. The method relies on the intrinsic fluorescence
(autofluorescence) of the target cells for generating detectable signal. Thus, this
method
does not use category-binding molecules or exogenous signaling moieties to achieve non-magnified
large area imaging of microscopic target cells. The advantages of reagent-less non-destructive
enumeration include generation of purified cultures (for microbial identification
and antibiotic susceptibility testing, improved method validation, and the ability
to follow microbial growth over time (for object discrimination and growth kinetics).
[0201] Experimental methods.
E. coli MG1655 cells were grown as in Example 1. Bacterial cells were diluted serially (ten-fold
dilutions) with sterile PBS. Bacterial cells (50 ml volume of the 10
-7 dilution) were deposited on a black polycarbonate filter (Osmonics; cat. num. K02BP04700)
using a vacuum filtration device and a plastic funnel cup (Millipore Microfil V User
Guide, PF07114, Rev A 3/00). A negative control was prepared by filtering sterile
PBS. After filtration, each filter was placed on a separate agar plate containing
LB growth medium, which was pre-warmed to 37°C, and placed in a 37°C incubator. The
viable cell count of the 10
-7 dilution was determined by filtering replicate samples and incubating the filter
on LB agar. This process indicated that the 10
-7 dilution contains approximately 1000 cells per 50 ml. At 5.25 hours, membranes were
imaged by placing the filters held on glass microscope slides into a CCD-based imager
(described in Step 5 of Detailed description section and shown in Figure 3) so that
the bacteria were facing the illumination source and CCD camera. A FITC optical filter
set (Chroma; excitation 470/40 nm, emission 522/40 nm) was used and a one second exposure
captured using software control (Image Pro Plus, version 4.1; Media cybernetics).
[0202] Results. Figure 7 shows the autofluorescence-based detection of bacterial microcoionies after
5.25 hours of growth. A filter containing microcolonies provided strong signals after
a one second exposure (Figure 7, left panel), while a filter lacking microcolonies,
but that was otherwise identically processed and imaged, did not exhibit such signals
(Figure 7, right panel).
[0203] The example demonstrates that this very simple embodiment of the invention is a powerful
approach for microbial growth detection. The technique could be used to make many
important microbial diagnostics applications more efficient including sterility testing,
environmental and water testing, microbial identification, and microbial susceptibility.
Example 3. A simple method for validating a rapid reagent-less microbial enumeration
test using an internal comparison to the traditional culture method
[0204] Background and objectives: Proving the equivalence of a new microbiological test to the "gold standard" method
is an essential task for both the developers of new methods and their customers. Formalized
validation requirements are generally codified in governmental regulations that guide
the introduction of new microbiological methods in industry and healthcare. New methods
for microbiological testing in the pharmaceutical industry have sometimes floundered
because of the difficulty of proving equivalence to the accepted methods. The goal
of this example is to demonstrate a simple method for proving the equivalence of a
test based on the invention to the traditional microbial culture test.
[0205] Experimental methods. E. coli MG1655 cells were grown and analyzed as in Example 2. After imaging the microcolonies,
the filter was re-incubated at 37°C for about 15 hrs. The resulting macrocolonies
were imaged using reflected white light supplied by an incandescent microscope lamp
shining obliquely on the plate. Otherwise, the same imaging system was used to collect
the reflected light as was used to detect microcolony autofluorescence.
[0206] Results. That this embodiment of the invention does not harm the microbes is apparent by comparing
the left and right panels of Figure 8. The exact correspondence between the "ancestral"
microcolonies (left panel) and their "descendant" macrocolonies (the right panel)
by this internal comparison should facilitate demonstration of equivalence to the
traditional microbial culture enumeration test.
Example 4. Accuracy and limit of detection of autofluorescent microcolony detection
using non-magnified large area imaging
[0207] Background and objectives: Accurate detection of small numbers of microbes is critical in both healthcare and
industrial microbiology. For example, only one bacterial cell in a 10 ml blood sample
may be present in a patient with a potentially fatal blood infection. Similarly, sterility
testing of an injectible drug in pharmaceutical manufacturing must detect a single
living microbial cell in a sample. In both cases, false negative results and false
positive results can have severe consequences. The fraction of test results that are
false positives and false negatives defines the accuracy of a test method.
[0208] The goal of this example is to show the accuracy of the invention at the lowest level
of target cells.
[0209] Experimental methods. E. coli MG1655 cells were grown and analyzed as in Example 3. However, for this example a
dilution of cells was applied to multiple filters (n = 101) so that on average the
detection zone on about one in five filters was expected to contain a single target
cell. After 5 hr of incubation each filter was imaged and scored for the presence
of microcolonies. The filters were then re-incubated overnight and scored for the
presence of macrocolonies. The results obtained using the invention were then compared
to the results obtained using traditional visual method.
[0210] Results. Figure 9 shows the method used in this example to measure the accuracy of the invention
when the samples contain the extremely low levels of target cells. For each of the
101 filters, the result obtained by scoring the microcolonies was the same as the
result obtained by the traditional method. As judged by either the presence of microcolonies
or macrocolonies, most filters (n = 80) had no deposited target cells. Furthermore,
the filters containing deposited target cells (n = 21), the microcolonies occurred
in the same numbers (several filters had multiple target cells, as would be expected
statistically) and with the identical placement on the filters as did the macrocolonies,
adding further robustness to the results. The invention and the "gold standard" method
were in 100% agreement, with no false positives or false negatives detected. Thus,
the results indicate that the invention is accurate at very low target cell levels.
Example 5. Determining the number of microbial cells in autofluorescent bacterial
microcolonies rapidly detected using reagent-less non-magnified imaging.
[0211] Background and objectives: The goal of this example is to demonstrate the sensitivity and speed of reagent-less
detection of microcolony autofluorescence using large area imaging. Rapid detection
of microbial growth is the result of the invention's ability to detect microcolonies
at early stages when the number of cells is small. The experiments in the example
determine number of bacterial cells in microcolonies detected by non-magnified CCD
imaging.
[0212] Experimental methods: A single colony of freshly grown
Escherichia coli (ATCC, Cat. No 8739) was inoculated into a conical tube (50 ml) containing growth
medium (TSB; 10 ml) and incubated (16 hours, 37°C, 150 rpm). This culture containing
stationary phase cells (2.4 x 10
9/ml) was used to inoculate an Erlenmeyer flask (500 ml) containing pre-warmed TSB
(37°C, 100 ml) to produce a log phase culture for optimal time to detection. This
flask, containing pre-warmed TSB was inoculated with the stationary phase culture
(100 µl) and incubated 2 hours, 37°C, 150 rpm). A culture established in this way
was found to contain ∼5x10
7 bacteria/ml via pour plate titration. The log phase culture was diluted in PBS (10
-6). A volume (10ml) of this dilution was filtered through a membrane (Chemunex Inc.,
Chemfilter CB0.4, Black, PET-23, 25 mm) supported over an absorbent pad (Whatman Inc.,
Cat. No. 1441325) using a filtration device (Millipore Inc., 1225 Sampling Manifold,
Cat. No. XX27 025 50). After the bacteria were collected on the membrane, the membrane
was placed on a pre-warmed TSA plate (32.5°C). An image of the plate was captured
(30 sec exposure) using non-magnified large area imaging with a FITC optical filter
set (Chroma; excitation 470/40 nm, emission 522/40 nm) using software control (Image
Pro Plus, Media Cybernetics). Following this initial image capture the plate was placed
in an incubator (32.5°C) for growth. The plate was removed from the incubator after
2.5 hours of growth and the same field was imaged again using the image capture settings
applied previously. Following image capture, the membrane was immediately fixed (1.5%
formaldehyde in filtered type 1 water for 5 minutes) followed by two washes (PBS,
5 min each) by placing the membrane on 3M Whatman paper impregnated with either fix
or wash solutions as indicated. The membrane was placed on the sampling manifold with
all but one placement blocked off with a stopper. Vacuum filtration was applied for
15 seconds. To stain the membrane in order to enumerate the bacteria in an individual
microcolony, propidium iodide (1 ml, 5 µg/ml) was added to the wall of the cup while
vacuum pressure was applied, followed by type 1 water (1 ml). Vacuum pressure was
applied for an additional 15 seconds after which the membrane was removed and placed
on a glass slide, dried, and mounted with a coverslip using the Pro Long Antifade
Reagent (Molecular Probes, Eugene, OR, Cat. No. P-4781). The stained microcolonies
were imaged using fluorescence microscopy (Axioplan II fluorescent microscope, Carl,
Zeiss Inc., Thornwood, NY; Cy3.5 filter set, Chroma Id. No. SP-103, excitation 581/10
nm, emission 617/40 nm, 400x) fitted with the SPOT RT camera (Diagnostic Instruments,
Sterling Heights, MI, Model No. 2.3.1, 2 seconds, red spectra only selected) following
spatial registration of these with their corresponding unstained microcolonies identified
using large area imaging.
[0213] Results: Figure 10 shows the results obtained in this example. Three microcolonies detected
after 2.5 hr of growth at 32.5°C using the invention were stained and analyzed using
high power microscopy. The three microcolonies contained 45, 48, and 50 cells, respectively.
No single bacterial cells were observed near the microcolonies demonstrating that
the microcolonies remained intact throughout the staining procedure. Note that visible
colonies of
E. coli (∼1 mm diameter) contain approximately one million times this number of cells. Thus,
using non-magnified reagent-less detection, the invention can detect microcolonies
after only a few generations of cell division.
Example 6. CCD-based, non-magnified, large area imaging detection and identification
of bacterial microcolonies in an environmental water sample
[0214] Background and objectives: This example aims to show the power of the invention for rapid detection of microbial
growth when applied to a variety of anonymous environmental microbes that are likely
to be nutritionally stressed.
[0215] Water is a common ingredient in the production, processing, and formulation of many
pharmaceuticals. Detection of bacteria in pharmaceutical water is a fundamental step
in the manufacturing process because the bacteria themselves or their metabolic products
could cause adverse consequences if is present in the final product. Proliferation
of bacteria may occur in the production, storage, and distribution of this substance.
Drinking water is the source feed water for pharmaceutical water and it is the major
exogenous source of microbial contamination. Fecal coliforms and a wide variety of
microorganisms, largely Gram-negative bacteria, may be present in drinking water.
The commonly used methods to detect bacteria in water are slow and thus hamper timely
system control.
[0216] Using non-magnified large area imaging to detect individual bacterial microcolonies
exploits the advantages of
in vitro replication analysis while avoiding the substantial disadvantages of traditional
and emerging methods. Advantages of
in situ replication analysis using the invention are: speed and the ability to detect and
identify without sacrificing microcolony viability (useful for identifying the source
of microbial contamination in a product or process or determining whether a particular
microorganism is harmful to the products or processes in which the water is used.)
[0217] Experimental overview. The example demonstrates the invention's ability to detect
in situ replication of bacterial microcolonies before these colonies grow into macrocolonies.
Bacteria are deposited on a filter and allowed to replicate
in situ. The resulting microcolonies and macrocolonies were detected using CCD-based, non-magnified,
large area imaging using autofluorescence (FITC excitation and emission filters) and
reflectance of white light.
[0218] Experimental methods. Water was aseptically collected from the Charles River (Cambridge, MA) and used
in the experiment within one hour of collection. The Charles River water was centrifuged
at a setting of 14,000 rpm in an Eppendorf Centrifuge 5415C for 1-2 seconds. The centrifuged
Charles River water was diluted 1:10 with sterile Type I water and 1.0 ml of this
was deposited on a black, mixed cellulose ester filter (Millipore; cat. num. HABP04700)
using a vacuum filtration device and a sterile plastic funnel (Millipore Microfil
® 100ml Funnel, cat. num. MIHABG072). Each filter after filtration was placed on a
separate agar plate containing R2A growth medium (Becton Dickinson/Difco; cat. num.
218263). Ten separate filters were prepared and the agar plates were incubated at
32.5°C for up to 74 hours. Periodically (after 17, 24, 42, 50, 68, and 74 hours) the
agar plates were removed from the incubator and the filters were imaged by placing
the plates on a CCD-based imager so that the bacterial colonies were facing the illumination
source and CCD chip. The illumination source for reflectance was provided by a Fiber-Lite
® Model 190 Convection Cooled 30 Watt Quartz Halogen Illuminator (Dolan-Jenner Industries,
Inc., Lawrence, MA), and the illumination was directed at an oblique angle onto the
filter. The naked eye is capable of seeing bacterial colonies that are 0.5 mm or greater
in diameter, so this size criterion was used as a discriminating characteristic of
a bacterial colony. The colonies that were 0.55 mm or greater in diameter were identified
and counted in the reflectance images. When autofluorescent microcolonies that gave
rise to a macrocolonies could be detected was also determined. Autofluorescent images
were analyzed to determine when the progenitors of 74 hr macrocolonies appeared. At
various time points the percentage the 74 hr macrocolonies that were detectable as
autofluorescent microcolonies was plotted.
[0219] Results. In this example bacterial cells from a water sample were allowed to replicate in
order to form microcolonies and macrocolonies. Both types of colonies were detected
by using the invention and identified by autofluorescence and reflectance. The data
shown in Figure 11 indicates that the number of colonies that can be visually observed
increased from 11 % (6 colonies) at 17 hr to 100% (53 colonies) at 74 hr. Ninety-four
percent (50 colonies) of the macrocolonies detected at 74 hours were detected as autofluorescent
microcolonies at 24 hours. This example shows that the invention can dramatically
decrease the time required for detection of bacterial growth and thus decrease the
amount of time needed for a bacterial test for water.
Example 7. Correlation between CCD-based, non-magnified, large area imaging detection
of bacterial microcolonies and a traditional method for enumerating bacteria
[0220] Background and objectives: The goal of this example is to determine the numerical correlation of the results
obtained using the present invention to detect microcolonies rapidly and those obtained
using slower traditional microbial culture.
[0221] Experimental objective: The example compares the enumeration of microcolonies by the invention and the classical
"pour plate" culture method. Bacteria were deposited on a filter and allowed to replicate
in situ. The resulting microcolonies were detected using CCD-based, non-magnified, large area
imaging using autofluorescence (FITC excitation and emission filters). The number
of microcolonies obtained with the invention was then compared to the number of macrocolonies
that were obtained with the pour plate method.
[0222] Experimental methods: E. coli 8739 cells were grown overnight in TSB to a density of approximately 10
9 cells/ml. Ten fold serial dilutions starting with approximately 10
7 cells/ml and ending with approximately 10
2 cells/ml of the overnight culture were made in PBS. An aliquot from each serial dilution
was further diluted with PBS such that 1.0 ml would contain approximately 50 bacteria.
One milliliter was placed in a petri dish together with 35 ml of melted (47°C) Tryptic
Soy Agar (TSA) (Becton Dickinson/Difco; cat. num. 236950). The agar plates were allowed
to cool at room temperature and then the plates were incubated overnight at 32.5°C.
Ten agar plates were prepared for each serial dilution. Macrocolonies in the agar
plates were counted by visually inspecting the plates. Dilutions of bacteria (11.3
ml) were deposited on a black mixed cellulose ester filter (Millipore; cat. num. HABP04700)
using a vacuum filtration device and a sterile plastic funnel (Millipore Microfil®
100 ml Funnel, cat. num. MIHABG072). Each filter was placed on a separate agar plate
containing TSA. Ten separate filters were prepared for each serial dilution, and the
agar plates were incubated at 32.5°C for 7 hours. The plates were then removed from
the incubator, and the filters were imaged by placing the plates on a CCD-based imager
so that the bacterial colonies were facing the illumination source and CCD chip. Autofluorescence
from each microcolony was detected using FITC excitation and emission filters. Eleven
times more volume was used with the filter because each image constitutes approximately
1/11
th of the entire filter surface. Thus, each image should contain approximately the same
number of bacteria as was put into each pour plate. The number of microcolonies in
each image was determined by visually inspecting the image. The number of bacteria
in each serial dilution was calculated by multiplying the number of microcolonies
or macrocolonies by a dilution factor.
[0223] Results: In this example bacterial cells were allowed to replicate and form microcolonies
on a filter or macrocolonies in agar plates. The microcolonies were detected using
the invention, and the macrocolonies were detected using a classical culture method
and visually inspecting the agar plates. The concentration of bacteria as determined
by each method for each serial dilution was plotted, and the results are shown in
Figure 12. Each point represents the average of ten separate determinations. A positive
correlation was obtained between the results obtained with the invention and the results
obtained with the classical pour plate method. The correlation coefficient of 0.9996
indicates a strong linear relationship between counting microcolonies with the invention
and macrocolonies with a classical culture method.
Example 8. Dynamic range and linearity of a reagent-less enumeration test
[0224] Background and objectives: Two of the validation criteria for a new microbiological testing method are the range
and linearity of the new method. The range is the interval between the upper and lower
levels of microorganisms that have been demonstrated to be determined with precision,
accuracy, and linearity using the new testing method. The linearity of a microbiological
test method is its ability to elicit results which are proportional to the concentration
of microorganisms present in the sample within a given range.
[0225] The example demonstrates the invention's linearity over a range of bacterial levels.
The invention detects the presence of microcolonies on the surface of a filter and
quantifies the autofluorescent signal of the microcolonies by using CCD-based, non-magnified,
large area imaging.
[0226] Experimental methods. E. coli 8739 cells were grown overnight in TSB to a density of approximately 10
9 cells/ml. Ten fold serial dilutions starting with a 10
-4 dilution of the overnight culture and ending with a 10
-9 dilution were made in PBS. Five ml of each serial dilution was deposited onto a black,
mixed cellulose ester filter (Pall Gelman Laboratory; cat. num. 66585) using a vacuum
filtration device and a sterile plastic funnel (Millipore Microfil® 100ml Funnel,
cat. num. MIHABG072). Each filter after filtration was placed on a separate agar plate
containing Trypticase Soy Agar with Lecithin and Polysorbate 80 (Becton Dickinson
BBL, cat. num. 211764). One filter was prepared for each serial dilution and then
the agar plates were incubated at 32.5°C for 6.5 hours followed by an overnight incubation
at 32.5°C. At the 6.5 hour time point, the agar plates were removed from the incubator,
and the filters were imaged by placing the plates on a CCD-based imager so that the
bacterial colonies were facing the illumination source and CCD chip. Autofluorescence
from each microcolony was detected using GFP excitation and GFP-LP emission filters.
The autofluorescent signal from the microcolonies in each image was quantified using
ImagePro software (Media Cybernetics, Inc., Version 4.5.0.19). Following the overnight
incubation, the agar plates were inspected visually, and the number of macrocolonies
present on the filters prepared with the 10
-8 and 10
-9 dilutions was counted. The number of macrocolonies on these two filters was used
to calculate the number of bacteria added to each membrane and the concentration of
bacteria in the initial overnight culture.
[0227] Results. In this example the bacterial cells were allowed to replicate and form microcolonies
on a filter in agar plates. The microcolonies were detected by using the invention
and identified by GFP-LP autofluorescence. The autofluorescent signal from the microcolonies
in each image was quantified using ImagePro software. The autofluorescent signal in
each image was plotted versus the number of bacteria added to each filter and the
results are shown in Figure 13. The data is linear over a 5 log range of bacteria
levels. This range is significant because the range of some classical culture methods
i.e. pour plates is only 2 logs. The results also show very strong linearity with
an R
2 value of 0.9929. This value is within the acceptable R
2 values (0.8 to 1.2) for a new microbiological testing method (Evaluation, Validation,
and Implementation of New Microbiological Testing Methods. 2000; PDA Journal of Pharmaceutical
Science & Technology 54
(Supplement TR33), 1-41).
Example 9. Rapid antimicrobial preservative effectiveness testing without sample dilutions
[0228] Background and objectives: Antimicrobial preservatives are added to articles packaged in multidose containers
to protect against growth of microorganisms that may be introduced by the manufacturing
process or by customers during withdrawal of individual doses. Antimicrobial effectiveness
must be demonstrated for pharmaceutical products that contain intrinsic antimicrobial
activity or products that contain an antimicrobial preservative. The tests are very
laborious and expensive to perform because of the large number of sample dilutions
that must be analyzed. Typically an antimicrobial preservative effectiveness test
requires analysis of
hundreds of microbial culture plates. An important goal of this example is to demonstrate
the potential of the invention to eliminate most of the labor of the test by obviating
the need for sample dilutions.
[0229] Experimental methods. E. coli 8739 cells were grown overnight in TSB to a density of approximately 10
9 cells/ml. Bacteria (8.48 x 10
6 total or 2.12 x 10
5 cells/ml)) were added to 40ml of sterile PBS or 40 ml of Osco Brand Sterile Preserved
Saline Solution (Distributed by American Procurement and Logistics Company, Lot num.
1T016, Exp. Jun 03). These two solutions were incubated at room temperature for 168
hours. After 0, 24, 96 and 168 hours, 5 ml of the PBS containing bacteria and 5 ml
of the Osco Saline containing bacteria were removed and added to separate tubes containing
45 ml of sterile D/E Neutralizing Broth (Becton Dickinson/Difco, cat num. 281910).
The diluted sample was then deposited onto a black, mixed cellulose ester filter (Pall
Gelman Laboratory; cat. num. 66585) using a vacuum filtration device and a sterile
plastic funnel (Millipore Microfil® 100 ml Funnel, cat. num. MIHABG072). Each filter
was placed on a separate agar plate containing Trypticase Soy Agar with Lecithin and
Polysorbate 80 (Becton Dickinson BBL, cat. num. 211764). One filter was prepared for
each solution at each time point. The agar plates were incubated at 32.5°C for 6.5
hours. The agar plates were removed from the incubator, and the filters were imaged
by placing the plates on a CCD-based imager so that the bacterial colonies were facing
the illumination source and CCD chip. Autofluorescence was detected using GFP excitation
and GFP-LP emission filters. The autofluorescent signal from the microcolonies in
each image was quantified using ImagePro software (Media Cybernetics, Inc., Version
4.5.0.19). Using the standard curve shown in Example 8, the autofluorescent signal
obtained by the ImagePro analysis was converted into the number of bacteria added
per membrane and then the concentration of bacteria per ml of solution (PBS or Osco
Saline) for each time point. Given the starting concentration of bacteria after 0
hours of incubation, the log decrease in bacterial concentration was calculated for
the 24, 96, and 168 hour time points. After 0, 24, 96, and 168 hours, 100 µl was removed
from the PBS and Osco Saline solutions containing bacteria and added to 900 µl of
D/E Neutralizing Broth (1:10 dilution). Serial 10 fold dilutions in 1.0 ml sterile
PBS were then made of the 1:10 dilution starting at 10
-1 and ending at 10
-6. The entire volume of the 10
-1 through 10
-6 dilutions was added to 30ml of melted (45°C) Trypticase Soy Agar with Lecithin and
Polysorbate 80. The agar plates were allowed to cool at room temperature and then
the plates were incubated overnight at 32.5°C. Bacterial colonies were visually counted
in the plates of the two lowest dilutions which contained less than 300 colonies per
plate. These numbers (multiplied by the appropriate dilution factor) were used to
calculate the concentration of bacteria in the PBS and Osco Saline solutions. Given
the starting concentration of bacteria after 0 hours of incubation, the log decrease
in bacterial concentration was calculated for the 24, 96, and 168 hour time points.
The log decrease in bacterial concentration as determined by the invention was plotted
versus the log decrease in bacterial concentration as determined by the pour plate
method (a classical, growth-based, microbiological enumeration method). The results
are shown graphically in Figure 14.
[0230] Results. The results in Figure 14 show that the antimicrobial preservative in the Osco Saline
solution (0.1 % Sorbic Acid) is effective in decreasing the concentration of bacteria.
No decrease in bacterial concentration was observed in the PBS. The data indicate
a linear correlation (R
2 = 0.9633) between the two enumeration methods even though no dilutions were required
by the invention. The results show the potential of the invention to save most of
labor and materials by eliminating the onerous sample dilutions of the traditional
method.
Example 10. Autofluorescence-based detection of a heat-stressed biological indicator
using non-magnified large area imaging
[0231] Background and objectives: The goals of this example are to show the potential application of the invention
for applications that use thermo-resistant spores as biological indicators. One important
application is sterilizer quantification methods for insuring the effectiveness of
sterilization procedures in pharmaceutical and medical device manufacture and in clinical
laboratories.
[0232] A further goal is to show the potential of the invention for simplifying biological
indicator enumeration by lowering the number of required samples. In the traditional
pour plate method, serial ten fold dilutions covering the entire possible range are
necessary to quantify samples accurately. In this example, non-magnified large area
imaging of the autofluorescence of the biological indicator
Geobacillus stearothermophilus is used to quantify the viable spore concentration. The quantification is linear
for about 3 orders of magnitude, decreasing the number of dilutions necessary to determine
the number of viable spores remaining after heat stress accurately. An autofluorescent
image is taken after a short period of growth, which is then analyzed to give an estimate
of the initial concentration of viable heat-stressed
G. stearothermophilus spores.
[0233] Experimental Methods: Spores of
G. stearothermophilus ATCC 7953 (Raven Biological Laboratories, Inc.) were diluted to a concentration of
∼2 x 10
5 spores/ml in sterile water and subjected to a variety of heat stresses ranging from
5 minutes at 110°C to 15 minutes at 121°C. The heat treated spores and an untreated
control were serially diluted by 10-fold in water up to a 1/1000 dilution. For comparison,
each sample was analyzed by the traditional pour plate method in addition to non-magnified
large area imaging of autofluorescence. Pour plates were prepared by placing 1 ml
of each dilution (including the undiluted stock) of each sample in a petri dish followed
by the addition of 20 ml of molten Trypticase Soy Agar (TSA, BD catalogue no. 236950).
After solidifying, the plates were incubated at 55°C for 48 hours and counted manually.
Plates that had between 30 and 300 colonies were used to calculate the spore titer,
unless no plates had more than 30 colonies, in which case the plate containing 1 ml
of undiluted stock was used.
[0234] To prepare microcolonies for large area imaging, 1 ml of the undiluted stock and
1 ml of the 1/100 dilution were mixed with 15 ml sterile water and filtered through
a black HABP filter (Millipore catalogue no. HABP04700) using vacuum filtration and
a plastic funnel cup (Millipore Microfil V User Guide, PF07114, Rev A 3/00). After
filtration, each filter was placed on a separate plate of TSA. Images were taken at
t=0 hours using the non-magnifying CCD-based imager (described in Step 5 of Detailed
description section and shown in Figure 3). Autofluorescence was captured using the
FITC optical filter set (Chroma; excitation 470/40 nm, emission 522/40 nm) with 5
second exposures. Plates were incubated at 55°C, and images were taken at 8 and 20
hours. Images were analyzed using Image Pro Plus software (version 4.1; Media cybernetics).
The t=0 exposures were used to find dust and other fluorescent contaminants that may
have been on the plates prior to growth. For each image, the sum of the pixel intensities
of all objects (where objects are defined in this particular example as containing
pixel intensities from 3200-65301 intensity units) minus the signal from contaminants
at t=0 were compared to a standard curve generated using unstressed spores, and the
spore titer was calculated from the standard curve. The value from either the undiluted
sample or the 1/100 dilution was used according to which one fell within the linear
range of the standard curve. The calculated values of spores/ml from autofluorescence
from non-magnified imaging were compared to the values calculated from the pour plate
method.
[0235] Results: A plot of the heat-stressed spore titer calculated from pour plates vs. spore titer
using autofluorescent large area imaging can be seen in Figure 15. There is a good
correlation between values from both methods, but four pour plates were necessary
for each two autofluorescent images. In addition, pour plates take 48-72 hours to
read, while the autofluorescent images can be taken and analyzed at 8-20 hours of
growth.
[0236] Variations. Non-magnified large area imaging of autofluorescence could also be used to quantify
viable cell concentrations of other biological indicator organisms, such as
Bacillus subtilis and
Clostridium sporogenes.
[0237] A variety of analyses of the autofluorescent images can be used to quantify cell
concentrations. For example, object counts of microcolonies can be used instead of
the sum of pixel intensities of the objects. Since the objects (microcolonies) are
much smaller than full grown macrocolonies (that can be counted by eye), more can
fit into the same area without sacrificing the accuracy that can be lost due to object
overlap. In addition, more sophisticated object finding algorithms can be applied
to the images to deal with local fluorescent background, touching objects, and presence
of contaminating fluorescent particles.
Example 11. Autofluorescence-based detection of bacterial microcolonies in ground
beef
[0238] Background and objectives: This example illustrates the ability of the invention to reduce the time to detection
of bacterial microcolonies in ground beef compared to compendial methods. Determination
of total viable bacteria count in raw meat is essential for preventing early food
spoilage. Current methods take two days, often requiring producers to ship the meat
before getting test results. Reducing the time to detection of microbes could prevent
foodborne disease incidents, manufacturing inefficiencies, and expensive recalls.
[0239] Experimental Methods: Lean ground beef (25 g) was diluted in 225 ml of 0.1% peptone water and processed
in a Stomacher to homogenize the sample. This sample was then diluted serially in
0.1% peptone water. Appropriate volumes of the 10
-2, 10
-3, 10
-4 and 10
-5 dilutions were added to PBS and then poured onto two filter membrane types (Millipore
HABP Cat. No. HABP04700 0.45 µm and Chemunex CB0.4 0.4 µm Ref. no. 200-C20010-01)
using vacuum filtration devices. Replicate samples were made for each dilution and
filter type and incubated on TSA plates at 35° C for 48 hrs. Images were captured
using a CCD-based imager at 0, 6, 16, 24, and 48 hrs. A FITC optical filter set (Chroma;
excitation 470/40 nm, emission 522/40 nm) was used and a 10 second image was captured
under HDyn resolution using software control (Image Pro Plus). Images were also captured
with white light reflectance for 10 seconds.
[0240] Results: Data was collected from the 10
-4 and 10
-5 dilutions on both membrane filter types. The data was analyzed by counting macrocolonies
at 48 hours that were 0.5 mm in diameter or larger in reflectance images. These macrocolonies
(≥ 0.5 mm) were then traced back to the 24, 16, and 6 hr time points, in reflectance
and autofluorescent images. Figure 16 shows the detection times of autofluorescent
microcolonies and macrocolonies. Tracking the appearance over time of microcolonies
that gave rise to the 48 hr macrocolonies showed that 100% of the macrocolonies were
detected by the invention by 16 hrs. These results show the potential of the invention
to reduce significantly the time required to achieve results compared to traditional
methods.
[0241] Variations. The test in this example can be extended to test a variety of foods,
including other meats, vegetables, beverages, and dairy products.
Example 12. Detection of bacteria in a complex sample with non-specific magnetic selection
followed by microcolony detection using non-magnified large area imaging
[0242] Objective: This example demonstrates an immunoassay method for selecting individual bacterial
cells, non-specifically, from a complicated sample followed by rapid detection of
growing microcolonies using non-magnified large area imaging. More specifically this
example demonstrates the ability to select a range of bacteria efficiently from blood
and then detect the growth of the bacteria using the growth direct method. This example
shows that magnetic beads coated with a mixture of binding agents, can select divergent
species of bacteria from a complex sample.
[0243] Experimental Methods: Figure 17 shows the process this example follows to detect bacteria in a complex
sample. First, bacterial cells and magnetic beads are added to the sample and incubated.
The magnetic beads are bound to the bacterial cells; then the complexes are sequestered
using magnetic force. The magnetic beads are resuspended (PBS), filtered, and plated
on growth media. The resulting magnetic selection supernatant is also plated. After
an incubation period, the filter is imaged at various time points using non-magnified
large area imaging to detect microcolonies.
[0244] .An array of magnetic particles were made by coupling magnetic particles with active
tosyl-groups (Dynal, Oslo, Norway, cat. no. 140.03) to several non-specific as well
as specific binding agents. The agents include polymyxin B sulfate (Sigma; cat. no.
P1004), polymyxin B nanoprotein (Sigma; cat. no. P2076), endotoxin neutralizing protein
(Seikagaku America: naturally derived and recombinant versions, cat. no. 910140-1,
910130-1 and 910120-1), endotoxin inhibitor protein (Bachem; cat. no. H-1382), endotoxin
substrate (Bachem; cat. no. L-1195), anti-lipotechoic acid antibody (QED; cat. no.
15711), anti-endotoxin antibody (QED cat. no. 15306 and 15301). The coated magnetic
particles (1x10
8 per 10 µl) were sonicated (1 min; setting 8; Fisher Scientific 550 Sonic Dismembrator).
Combinations of the coated magnetic beads were then added to 1.5 ml tubes of blood
(1 ml, Biochemed; Human blood, sodium citrate as anticoagulant, cat. no. 10762WB)
spiked with approximately 1, 10 or 100 cells of
Staphylococcus aureus (ATCC # 27694). The blood, bacteria and magnets were allowed to incubate (1 hour
at room temp). After incubation the beads were magnetically selected using a magnetic
separation device (Polysciences, Inc., Warrington, PA, Cat. No. 8MB4111 S) to capture
and secure the magnetic particles. The blood was then decanted and plated on TSA (Difco,
cat. no. 236950) as was the initial Staphylococcus aureus inoculums of 1, 10 and 100
cells (used as controls). The magnetic particles were resuspended (1 ml PBS) and the
resulting liquid containing magnetic particles-bacterial complexes was filtered onto
a membrane (Osmonics, poretics 47 mm, 0.22 µm pore, polycarbonate black filter, cat.
no. 1213889), and the membrane was then placed on a TSA plate. At both the zero time
point and after a short incubation period, the filters were imaged using non-magnified
large area imaging to detect the autofluorescent microcolonies. The percent recovery
was determined by comparing the inoculum count with the magnetic capture count and
using the formula: (average magnetic capture/average inoculum count) X 100.
[0245] Results: Figure 18 shows the experimental results demonstrating microcolony detection after
the magnetic separation. This figure shows two images, taken at after zero and six
hours of growth. The six hour image has putative microcolonies - these are bright
spots that are not seen in the zero image. To confirm that these are indeed growing
microbial microcolonies, the filters were allowed to incubate overnight and re-imaged.
Macrocolonies were detected at the positions of the putative microcolonies confirming
the rapid result. Greater than 90 percent recovery of Staphylococcus aureus was achieved
for the 1 cell samples. The 10 and 100 cell sample had greater than 50 percent recovery.
[0246] Variations (broad binding agents): Numerous broadly reactive binding agents could be used including wheat germ agglutinin,
anti-enterobacterial common antigen, anti-protein A, anti-protein G, LPS binding protein,
mucin (bacterial binding agent), CD14 (binds both LPS and LPS bacterial complexes),
collectins (these bind bacteria during phagocytosis or during the complement cascade),
subunits of complement itself such as C3b and C4b, human scavenger receptors (cell
receptors that bind bacterial components) and tectonics (carbohydrate binding proteins).
[0247] Variations (specific binding agents): A variety of types of category-binding molecules, including antibodies, aptamers,
and ligands, can be used to specifically select a range of cells types from complex
samples. In this example variation, selection of an
E. coli 0157:H7 is achieved using an
E. coli 0157:H7 specific antibody.
[0248] Variation of Experimental Method: In this variation, detection of bacteria in a complex sample is achieved with analyte-specific
magnetic selection. The selection is followed by microcolony detection using non-magnified
large area imaging. Figure 17 shows the method to use for this example. A sample containing
E. coli O157:H7 is mixed with magnetic particles. The sample is then magnetically selected,
filtered and imaged at a series of time points using non-magnified large area imaging.
Anti-
E. coli 0157:H7 magnetic particles are made by coupling tosyl-modified magnetic particles (Dynal,
Oslo, Norway, cat. No. 140.03; coupling performed according to manufacturer's recommendations)
to polyclonal antibodies raised against
E. coli 0157:H7 (BioTrace affinity purified; Kirkegaard & Perry Laboratories, Gaithersburg, MD, Cat.
No. 01-95-90). Anti-
E. coli 0157:H7 magnetic particles (1x10
8/10 µl) were sonicated (1 min; setting 8; Fisher Scientific 550 Sonic Dismembrator).
The magnetic beads are then added to blood (1 ml; Biochemed; Human blood, sodium citrate
as anticoagulant, cat. no. 10762WB) spiked with
E. coli 0157:H7 (Strain DEC 3B, Dr. Tom Whittam, Pennsylvania State University).
E. coli 0157:H7 microcolony growth and detection are achieved follow the same steps used above in
this example.
Example 13. Antimicrobial susceptibility testing using in situ replication and non-magnified large area imaging
[0249] Background and objectives: The significance of antimicrobial susceptibility testing for determining appropriate
therapy is discussed in the background section. Monitoring microbial growth on solid
medium is common and has some significant advantages over growth in liquid culture.
It is possible to inexpensively, simultaneously, and quantitatively determine the
susceptibility of a strain of bacteria to several antibiotics without the use of instrumentation
(e.g., using disk diffusion assays), but the current methods require a purified colony
and thus cannot usually be performed for 1-2 days after the patient's sample has been
processed. Such delays can be life threatening. Furthermore, another 1-2 days is generally
required to detect and analyze the result of a antimicrobial susceptibility test.
[0250] Objective. The example demonstrates the use of the invention to determine the antibiotic susceptibility
of bacterial strains rapidly. The principle of the method is diagrammed in Figure
19. In the experiment described below, resistant and sensitive strains were grown
on media with and without antibiotic and microcolonies were detected as in the previous
example. The approach offers the potential to shorten significantly the prolonged
growth steps (colony purification and growth in antibiotic) that currently can delay
implementation of appropriate antimicrobial therapy.
[0251] Methods. A sensitive (
E. coli MG1655) and resistant
(E. coli MG1655/ pLafr I) strain of bacteria were deposited on filters as in the previous example
(Example 1). Filters containing approximately 1000 resistant bacteria were placed
on LB plates (LB agar; Difco) that either contained antibiotic (tetracycline; 64 µg/ml)
or did not contain antibiotic. After incubation at 37°C (3 hrs), the filters were
stained and imaged as in (Example 1).
[0252] Results. Figure 21 shows the results of antimicrobial susceptibility testing using CCD-based
non-magnified large area imaging. CCD imaging detected microcolonies on the membrane
containing resistant bacteria but not on the membrane containing sensitive bacteria
(compare the rows labeled "resistant strain" and "sensitive strain" in the leftmost
column labeled: 3 hour + tet, CCD). The intensity data obtained from image analysis
quantified this observation (bar graph). High power fluorescence microscopy confirmed
that the resistant strain formed microcolonies after 3 hours of incubation, while
the sensitive strain did not. (Microscopic analysis indicated that incubation of the
sensitive strain in the presence of antibiotic leads to aberrant bacterial morphologies
[compare the two microscopic images in the bottom row labeled "sensitive strain"].).
[0253] The results of this experiment show that detecting microcolonies using non-magnified
large area imaging is a rapid and sensitive method for antimicrobial susceptibility
testing.
[0254] Variations: Some variations on the antimicrobial susceptibility test include using different
signal moieties. Viability stains, such as Syto 9 and other Syto family members (Molecular
Probes), esterase substrates such as fluorescein diacetate or chemchrome V6 (Chemunex),
labeled antibodies, or metabolites that yield fluorescent products, could be substituted
for the nucleic acid stain in this assay. The natural autofluorescence of the cellular
target cells could also be used to detect the microcolonies. Microcolony growth could
also be used to monitor geometrical growth constraints as with antimicrobial susceptibility
testing disk diffusion or the E test methods (AB biodisk NA Inc.; E-test strips).
The antimicrobial susceptibility assay can also be expanded to include simultaneous
identification of various microbes with different fluorescently labeled antibodies.
Example 14. Rapid antimicrobial susceptibility testing using the disk diffusion method
and non-magnified large area imaging
[0255] Objective: This example demonstrates the use of the invention to determine the antibiotic susceptibility
of bacterial strains rapidly using the disk diffusion method. Disks that are impregnated
with a known concentration of an antibiotic are placed on plates containing a large
number of cells from a purified microbial culture. The antibiotic diffuses from the
disk cresting a radial gradient of antibiotic concentration centered on the disk (
i.
e., the closer to the disk, the higher the concentration of antibiotic). Highly resistant
strains can grow in the presence of the disks even near the edge where the antibiotic
concentration is highest. Less resistant strains grow outside of a zone of inhibition
surrounding the disk. The width of the zone of inhibition is correlated with the level
of antibiotic resistance for the strain.
[0256] The zone of inhibition is traditionally measured by the naked eye after an overnight
growth. This example demonstrates the ability to determine the zone of inhibition
in hours by detecting the growth of microcolonies using non-magnified large area imaging.
[0257] Experimental Methods: The strains, used in the example and described in Example 13, were diluted to 10
6 CFU/ ml and plated on TSA media. A tetracycline diffusion disk (Hardy Diagnostics;
30 µg tetracycline, cat. no. Z9121) was then placed on the plates. The plates were
allowed to incubate at 37°C for 5 hours. The microcolonies were imaged using microcolony
autofluorescence and non-magnified large area imaging as in previous examples.
[0258] Results: Figure 21 shows the results of a rapid antimicrobial susceptibility test using non-magnified
large area imaging. The CCD-based imaging detected autofluorescent resistant colonies
growing near an antibiotic diffusion disk after only 5 hours. The zone of inhibition
was comparable to that obtained by visual inspection after overnight growth. The results
of this experiment show that detecting zones of inhibition based on microcolony growth
is more rapid than the traditional disk diffusion method but can yield comparable
results.
[0259] Variations: This technique can be used with most antibiotic diffusion disks and most microbes.
Example 15. Rapid antimicrobial susceptibility testing using the E-test™ and non-magnified large area imaging
[0260] Objective: This example demonstrates the use of the invention to rapidly determine the antibiotic
susceptibility of bacterial strains using an E-test
™ antibiotic test strip. The E-test
™ strip is impregnated with a range of concentrations of tetracycline enabling the
user to use one strip to determine the lowest antibiotic concentration needed to inhibit
the growth of the tested bacteria. This minimal inhibitory concentration is based
on the visualization of zones with no growth, called the zone of inhibition. The zone
of inhibition is traditionally measured by the naked eye after an overnight growth.
This example demonstrates the ability to determine the zone of inhibition in hours
by detecting the growth of microcolonies using non-magnified large area imaging.
[0261] Experimental Methods: The strains, used in the example and described in Example 13, were diluted to 10
6 CFU/ ml and plated on TSA media.. The E-test
™ strip (Hardy diagnostics: 0.016-256 µg tetracycline, cat. no. 51002258) was then
placed on the plates which were allowed to incubate at 37°C for five hours. The microcolonies
growing on or near the test strip were imaged using microcolony autofluorescence and
non-magnified large area imaging as in previous examples. After imaging, the plates
were allowed to incubate overnight.
Results:
[0262] Figure 22 shows the results of a rapid antimicrobial susceptibility E-test
™ using non-magnified large area imaging. Non-magnified, large area imaging detected
autofluorescent resistant microcolonies growing near the E-test
™ antibiotic test strip. A zone of inhibition comparable with that observed after overnight
growth could be determined after five hours of growth. The results of this experiment
show that detecting microcolonies using non-magnified large area imaging is a rapid
and sensitive method greatly reducing the time to result for an E-test
™.
[0263] Variations: This technique is applicable to E-Test™ strips impregnated with a variety of antibiotics.
1. A method for detecting living target cells in a sample, said method comprising the
steps of:
(a) providing living target cells present in said sample in a detection zone comprising
a detection area at a density of less than 100 target cells per mm2 of the detection area, wherein within said detection zone said cells are randomly
dispersed and immobilized;
(b) allowing the formation of one or more microcolonies of said target cells by in
situ replication; and
(c) detecting said one or more microcolonies;
wherein the longest linear dimension of said detection area is greater than 1 mm;
said one or more microcolonies have a mean measurement of less than 50 microns in
at least two orthogonal dimensions; said detecting does not entail magnification of
more than 5x; said detecting detects a property of said one or more microcolonies
that does not depend on the addition of a signalling moiety or category binding molecule;
and said cells in said one or more microcolonies remain competent to replicate following
said detecting.
2. The method of claim 1, wherein said target cells are randomly dispersed in a detection
zone at a density of less than 10 target cells per mm2 of the detection area.
3. The method of claim 1, wherein said target cells are randomly dispersed in a detection
zone at a density of less than 1 target cells per mm2 of the detection area.
4. The method of claim 1, wherein said detecting detects a single microcolony in the
detection area.
5. The method of claim 1, wherein said detecting detects overlapping or contiguous microcolonies.
6. The method of claim 1, wherein said detecting does not entail magnification of more
than 2x.
7. The method of claim 1, wherein said detecting does not entail magnification of more
than 1x.
8. The method of claim 1, wherein said detecting does not entail magnification of more
than 0.2x.
9. The method of claim 1, wherein the mean number of cells in said one or more microcolonies
is less than 50,000 cells.
10. The method of claim 9, wherein said one or more microcolonies comprise less than 10,000
cells.
11. The method of claim 9, wherein the mean number of cells in said one or more microcolonies
is less than 1000.
12. The method of claim 9, wherein the mean number of cells in said one or more microcolonies
is less than 100.
13. The method of claim 9, wherein the mean number of cells in said one or more microcolonies
is less than 10.
14. The method of claim 9, wherein said one or more microcolonies have a mean measurement
of less than 25 microns in the longest linear dimension.
15. The method of claim 9, wherein said one or more microcolonies have a mean measurement
of less than 10 microns in the longest linear dimension.
16. The method of claim 1, wherein said target cells are bacteria.
17. The method of claim 1, wherein said target cells are eukaryotic cells.
18. The method of claim 17, wherein said target cells are mold or fungal cells.
19. The method of claim 17, wherein said target cells are human, animal, or plant cells.
20. The method of claim 1, wherein said target cells are parasites of humans, animals,
or plants.
21. The method of claim 1, wherein said detecting detects and identifies microcolonies
from more than one non-overlapping category of cell.
22. The method of claim 1, wherein said sample comprises fluids or tissues obtained from
a multicellular organism.
23. The method of claim 1, wherein said sample comprises the bodily fluids or tissues
of an animal.
24. The method of claim 1, wherein said sample is derived from a human.
25. The method of claim 1, wherein said sample is derived from a non-human vertebrate.
26. The method of claim 1, wherein said sample is selected from the group consisting of:
respiratory, urogenital, reproductive tract, central nervous system, urine, blood,
dermal, plasma, serum, saliva, wound tissue, wound exudate, biopsy, feces, reproductive
tract, and solid tissue samples, and derivatives thereof.
27. The method of claim 1, wherein said sample is a blood or urine sample.
28. The method of claim 1, wherein said sample is derived from a plant.
29. The method of claim 1, wherein said sample is obtained by sampling environmental air
or water, or surfaces, objects, or organisms exposed to the environment.
30. The method of claim 1, wherein said sample is obtained from a material selected from
the group consisting of raw, finished, or in-process material in the manufacture of
pharmacological, cosmetic, blood, or other products for topical or internal use in
humans or animals; raw, in-process, or finished material in the manufacture of foods
or beverages; raw, in-process, or finished material in the manufacture of medical
or in vitro diagnostic devices, chemical products; industrial surfaces; instrumentation;
and machinery.
31. The method of claim 1, wherein said detection zone is contacted with a liquid medium
comprising one or more substances that facilitate replication of target cells.
32. The method of claim 1, wherein said cells are deposited directly on a solid or semi-solid
growth medium.
33. The method of claim 1, wherein, prior to step (a), a selection method is used to deposit
complexes of one or more of said target cells and a selection moiety in said detection
zone, wherein said selection method is selected from the group consisting of magnetic
selection, centrifugation, settling, and filtration.
34. The method of claim 33, wherein, prior to step (a), said target cells are contacted
with target cell-specific magnetic selection moieties and complexes of one or more
of said target cells and said selection moiety are subsequently deposited on said
detection surface using magnetic force.
35. The method of claim 34, wherein said target cell-specific magnetic selection moieties
comprise magnetic particles that are conjugated to category-binding molecules.
36. The method of claim 33, wherein said target cells are contacted in a liquid with said
target-cell specific selection moieties that have an average density greater than
the average density of said liquid and wherein complexes of one or more of said target
cells and said selection moiety are subsequently deposited on said detection surface
using gravitational, centrifugal, or centripetal force.
37. The method of claim 1, wherein said target cells are deposited in said detection zone
using a selection method selected from the group consisting of magnetic selection,
centrifugation, settling, and filtration, wherein a selection moiety is not employed.
38. The method of claim 1, wherein, prior to step (a), said sample is treated to liquefy
and/or homogenize said sample.
39. The method of claim 1, wherein, prior to step (a), said sample is treated to remove
substances or objects other than said target cells.
40. The method of claim 1, further comprising the step of determining the effect of one
or more substances or treatments on one or more attributes of said target cells.
41. The method of claim 40, wherein said attribute is the ability to undergo cell replication.
42. The method of claim 40, wherein said one or more substances are present in a medium
used to support the replication of said target cells.
43. The method of claim 40, wherein said attribute is the ability of said target cells
to replicate following a sterilization treatment.
44. The method of claim 40, wherein said attribute is the ability of said target cells
to replicate in the presence of one or more potential inhibitors of replication.
45. The method of claim 40, wherein said target cells are bacteria, fungi, parasites,
or cultured cells, and said substances are antibacterial agents, agents, antifungal
agents, or anti-parasitic agents.
46. The method of claim 44, wherein said one or more inhibitors are antimicrobial compounds.
47. The method of claim 44, wherein said one ore more inhibitors are anti-tumor compounds.
48. The method of claim 40, wherein said attribute is the viability, change in an optical
property, metabolic or enzymatic activity, or biochemical constituency of said target
cells.
49. The method of claim 34, further comprising the steps of:
(d) contacting said target cells with one or more substances or treating said cells
with one or more treatments; and
(e) determining the effect of said one or more substances or said one or more treatments
on one or more attributes of said target cells.
50. The method of claim 1, wherein said detection zone comprises a material selected from
the group consisting of glass, plastic, the surface of wells of microtiter plates,
bibulous membranes, plastic strips, the surfaces of capillary tubes, the surfaces
of microfluidic chambers, and the surfaces or microfluidic channels.
51. The method of claim 1, wherein the replication of said cells in said microcolonies
is continued after said detecting.
52. The method of claim 1, wherein step (c) comprises at least two cycles each of which
comprises a period in which cells are allowed to replicate followed by a detection
step.
53. The method of claim 1, further comprising the step of repeating steps (a) - (c) with
one or more additional samples, wherein said repeating is automated.
54. The method of claim 53, wherein said samples are automatically loaded into an instrument
that comprises a detector.
55. The method of claim 53, wherein said samples are automatically deposited in a series
of detection zones that are physically associated and that are automatically and successively
loaded into an instrument that comprises a detector.
56. The method of claim 1, wherein said detecting comprises illuminating one or more microcolonies
to generate a detectable signal.
57. The method of claim 56, wherein said detecting detects light emitted, scattered, reflected,
or absorbed as a result of illumination of said one or more microcolonies.
58. The method of claim 1, wherein said detecting detects fluorescence.
59. The method of claim 58, wherein said fluorescence is autofluorescence emitted by said
microcolonies.
60. The method of claim 56, wherein said illuminating employs one or more lasers.
61. The method of claim 56, wherein said illuminating employs one or more light-emitting
diodes.
62. The method of claim 56, wherein said illuminating employs a source of white-light.
63. The method of claim 56, wherein said illuminating is through one or more optical filters
that only pass selected wavelengths of light.
64. The method of claim 1, further comprising the step, prior to or during step (c), of
contacting said sample with a signalling moiety that associates either directly or
indirectly with said target cells.
65. The method of claim 64, wherein said signalling moiety is associated with a category-binding
molecule.
66. The method of claim 1, further comprising the step, prior to or during step (c), of
contacting said sample with a category-binding molecule under conditions that allow
the formation of one or more complexes between said category-binding molecule and
one or more category-specific binding sites on one or more of said target cells.
67. The method of claim 66, wherein said category-binding molecule comprises an antibody,
aptamer, or ligand.
68. The method of claim 66, wherein said category-binding molecule is labeled, either
directly or indirectly, with one or more signalling moieties.
69. The method of claim 68, further comprising the step, prior to step (c), of removing
any unbound category-binding molecules from said one or more complexes.
70. The method of claim 66, wherein said category binding molecule is a member of an ensemble
of category-binding molecules, wherein said ensemble comprises one family of category-binding
molecules specific for each non-overlapping category of target cells to be detected.
71. The method of claim 70, wherein each of said families of category-binding molecules
is labeled with a signalling moietie that emits a signal of a distinct signal class
or signal signature.
72. The method of claim 65, wherein said signalling moiety is a particle or is physically
associated with a particle.
73. The method of claim 64, wherein said signalling moiety has fluorescent signalling
character.
74. The method of claim 73, wherein said signalling moiety is selected from the group
consisting of organic fluorophores, up-regulated phosphors, lanthanides, quantum dots,
enzymes that generate fluorescent product from non-fluorescent substrates, and fluorescently
dyed particles.
75. The method of claim 73, wherein said signalling moiety is a fluorescent stain for
cells.
76. The method of claim 64, wherein said signalling moiety has chromogenic signalling
character.
77. The method of claim 64, wherein said signalling moiety has chemiluminescent signalling
character.
78. The method of claim 64, wherein said signalling moiety has light-scattering signalling
character.
79. The method of claim 78, wherein said signalling moiety is a resonance light scattering
particle or plasmon resonance particle.
80. The method of claim 64, wherein said signalling moiety is a viability stain for staining
living cells.
81. The method of claim 70, wherein said ensemble of category-binding molecules has a
family complexity of 1.
82. The method of claim 70, wherein said ensemble of category-binding molecules has a
family complexity that is greater than 1.
83. The method of claim 82, wherein said ensemble has a family complexity ≥ 5.
84. The method of claim 64, wherein said signalling moiety comprises one or more compounds
that are not detectable until upon association with said target cells, said signalling
moiety is acted on by a constituent of said target cells or by a physiological, physical,
or micro-environmental state of said target cells.
85. The method of claim 1, wherein said replication and said detecting occur in a vessel
constructed so as not to allow additional cells to enter or cells in the sample to
exit.
86. The method of claim 1, wherein said replication and said detecting occur in a vessel
that has a bar code or equivalent label for tracking the sample automatically.
87. The method of claim 1, wherein said replication and said detecting occur on a surface
with registration marks to facilitate alignment of multiple images of the same surface.
88. The method of claim 1, wherein said detecting detects control marks or control cells
in a specified region of the detection zone.
89. The method of claim 1, wherein said detecting employs optical filters adapted to detect
a signal derived from the illumination of said target cells .
90. The method of claim 1, wherein said detecting employs a photoelectric detector.
91. The method of claim 1, wherein said detecting employs a photoelectric array detector.
92. The method of claim 91, wherein said photoelectric detector comprises a CCD detector.
93. The method of claim 1, wherein said detecting does not employ an image intensifier.
94. The method of claim 1, wherein said detecting employs a photomultiplier tube detector.
95. The method of claim 1, wherein said detecting employs a photodiode detector.
96. The method of claim 1, wherein said detecting employs a photosensitive film.
97. The method of claim 1, further comprising the step, during or after step (c), of quantifying
the number of microcolonies.
98. The method of claim 1, further comprising the step, during or after step (c), of determining
the category of said target cells by analyzing an image of said detection area using
image analysis software.
99. The method of claim 1, further comprising the step, during or after step (c), of determining
the locations in the detection zone of said one or more microcolonies by analyzing
an image of said detection area using image analysis software.
100. The method of claim 99, further comprising the step, during or after step (c), of
comparing said locations in the detection zone of individual microcolonies to previously
determined locations of the same microcolonies.
101. The method of claim 100, wherein said image analysis software comprises algorithms
for discerning objects that change size over time from objects that do not change
size over time.
102. The method of claim 97, wherein said determining comprises analyzing an image of said
detection area.
103. The method of claim 43, wherein said sterilization treatment is selected from the
group consisting of heat sterilization, irradiation, toxic gas exposure, and disinfectant
treatment.
1. Verfahren zum Detektieren lebendiger Zielzellen in einer Probe, wobei das Verfahren
die folgenden Schritte umfasst:
(9) Bereitstellen lebendiger Zielzellen, die in der Probe vorhanden sind, in einer
Detektionszone, die einen Detektionsbereich mit einer Dichte von weniger als 100 Zielzellen
pro mm2 des Detektionsbereichs umfasst, wo die Zellen innerhalb der Detektionszone zufällig
dispergiert und immobilisiert werden;
(b) Gestatten der Bildung einer oder mehrerer Mikrokolonien der Zielzellen durch Replikation
in situ; und
(c) Detektieren der einen oder mehreren Kolonien;
wo die längste lineare Dimension des Detektionsbereichs größer als 1 mm ist; die eine
oder mehreren Mikrokolonien ein Durchschnittsmaß von weniger als 50 Mikrometer in
mindestens zwei orthogonalen Dimensionen hat/haben; das Detektieren keine Vergrößerung
von mehr als 5x beinhaltet; eine Eigenschaft der einen oder mehreren Mikrokolonien,
die nicht vom Zusatz einer Signalgruppe oder eines Kategoriebindungsmoleküls abhängt,
beim Detektieren detektiert wird; und die Zellen in der einen oder mehreren Mikrokolonien
fähig bleiben, nach dem Detektieren zu replizieren.
2. Verfahren nach Anspruch 1, wo die Zielzellen in einer Detektionszone mit einer Dichte
von weniger als 10 Zielzellen pro mm2 des Detektionsbereichs zufällig dispergiert werden.
3. Verfahren nach Anspruch 1, wo die Zielzellen in einer Detektionszone mit einer Dichte
von weniger als 1 Zielzelle pro mm2 des Detektionsbereichs zufällig dispergiert werden.
4. Verfahren nach Anspruch 1, wo eine einzige Mikrokolonie im Detektionsbereich beim
Detektieren detektiert wird.
5. Verfahren nach Anspruch 1, wo überlappende oder an einander angrenzende Mikrokolonien
beim Detektieren detektiert werden.
6. Verfahren nach Anspruch 1, wo das Detektieren keine Vergrößerung von mehr als 2x beinhaltet.
7. Verfahren nach Anspruch 1, wo das Detektieren keine Vergrößerung von mehr als 1x beinhaltet.
8. Verfahren nach Anspruch 1, wo das Detektieren keine Vergrößerung von mehr als 0,2x
beinhaltet.
9. Verfahren nach Anspruch 1, wo die Durchschnittszahl von Zellen in der einen oder mehreren
Mikrokolonien weniger als 50.000 Zellen ist.
10. Verfahren nach Anspruch 9, wo die eine oder mehreren Mikrokolonien weniger als 10.000
Zellen umfasst/umfassen.
11. Verfahren nach Anspruch 9, wo die Durchschnittszahl von Zellen in der einen oder mehreren
Mikrokolonien weniger als 1000 ist.
12. Verfahren nach Anspruch 9, wo die Durchschnittszahl von Zellen in der einen oder mehreren
Mikrokolonien weniger als 100 ist.
13. Verfahren nach Anspruch 9, wo die Durchschnittszahl von Zellen in der einen oder mehreren
Mikrokolonien weniger als 10 ist.
14. Verfahren nach Anspruch 9, wo die eine oder mehreren Mikrokolonien ein Durchschnittsmaß
von weniger als 25 Mikrometer in der längsten linearen Dimension hat/haben.
15. Verfahren nach Anspruch 9, wo die eine oder mehreren Mikrokolonien ein Durchschnittsmaß
von weniger als 10 Mikrometer in der längsten linearen Dimension hat/haben.
16. Verfahren nach Anspruch 1, wo die Zielzellen Bakterien sind.
17. Verfahren nach Anspruch 1, wo die Zielzellen eukaryotische Zellen sind.
18. Verfahren nach Anspruch 17, wo die Zielzellen Schimmel- oder Pilzzellen sind.
19. Verfahren nach Anspruch 17, wo die Zielzellen Menschen-, Tier- oder Pflanzenzellen
sind.
20. Verfahren nach Anspruch 1, wo die Zielzellen Parasiten von Menschen, Tieren oder Pflanzen
sind.
21. Verfahren nach Anspruch 1, wo Mikrokolonien aus mehr als einer nicht-überlappenden
Kategorie von Zellen beim Detektieren detektiert und identifiziert werden.
22. Verfahren nach Anspruch 1, wo die Probe Flüssigkeiten oder Gewebe aus einem multizellulären
Organismus umfasst.
23. Verfahren nach Anspruch 1, wo die Probe Körperflüssigkeiten oder -gewebe eines Tiers
umfasst.
24. Verfahren nach Anspruch 1, wo die Probe von einem Menschen stammt.
25. Verfahren nach Anspruch 1, wo die Probe von einem nicht-menschlichen Wirbeltier stammt.
26. Verfahren nach Anspruch 1, wo die Probe aus der Gruppe bestehend aus: Proben vom Respirationstrakt,
Urogenitaltrakt, Reproduktionstrakt, Zentralnervensystem, von Urin, Blut, Haut, Plasma,
Serum, Speichel, Wundgewebe, Wundexsudat, Biopsien, Fäzes sowie Proben von festem
Gewebe und Derivaten hiervon ausgewählt ist.
27. Verfahren nach Anspruch 1, wo die Probe eine Blut- oder Urinprobe ist.
28. Verfahren nach Anspruch 1, wo die Probe von einer Pflanze stammt.
29. Verfahren nach Anspruch 1, wo die Probe dadurch erhalten wird, Proben aus Umgebungsluft oder Umgebungswasser oder von Oberflächen,
Gegenständen oder Organismen, die der Umgebung ausgesetzt werden, zu entnehmen.
30. Verfahren nach Anspruch 1, wo die Probe aus einem Material entnommen wird, das aus
der Gruppe bestehend aus rohem oder fertig bearbeitetem Material oder Material in
Bearbeitung bei der Herstellung von pharmakologischen Produkten, Kosmetikprodukten,
Blutprodukten oder anderen Produkten für topische oder innere Anwendung bei Menschen
oder Tieren; rohem oder fertig bearbeitetem Material oder Material in Bearbeitung
bei der Herstellung von Lebensmitteln oder Getränken; rohem oder fertig bearbeitetem
Material oder Material in Bearbeitung bei der Herstellung von medizinischen oder in
vitro diagnostischen Einrichtungen, chemischen Produkten, industriellen Oberflächen,
Geräteausstattung und Maschinen ausgewählt ist.
31. Verfahren nach Anspruch 1, wo die Detektionszone mit einem flüssigen Medium in Kontakt
gebracht wird, das einen oder mehrere Stoffe, die Replikation von Zielzellen erleichtern,
umfasst.
32. Verfahren nach Anspruch 1, wo die Zellen direkt auf ein festes oder halbfestes Wachstumsmedium
abgesetzt werden.
33. Verfahren nach Anspruch 1, wo, vor Schritt (a), ein Selektionsverfahren verwendet
wird, um Komplexe der einen oder mehreren Zielzellen und einer Selektionsgruppe in
der Detektionszone abzusetzen, wo das Selektionsverfahren aus der Gruppe bestehend
aus magnetischer Selektion, Zentrifugation, Ablagerung und Filtration ausgewählt ist.
34. Verfahren nach Anspruch 33, wo, vor Schritt (a), die Zielzellen mit zielzellenspezifischen
magnetischen Selektionsgruppen in Kontakt gebracht werden, und Komplexe der einen
oder mehreren Zielzellen und der Selektionsgruppe anschließend auf die Detektionsoberfläche
unter Anwendung magnetischer Kraft abgesetzt werden.
35. Verfahren nach Anspruch 34, wo die zielzellenspezifischen magnetischen Selektionsgruppen
magnetische Partikel umfassen, die mit Kategoriebindungsmolekülen konjugiert werden.
36. Verfahren nach Anspruch 33, wo die Zielzellen in einer Flüssigkeit mit den zielzellenspezifischen
Selektionsgruppen in Kontakt gebracht werden, die eine Durchschnittsdichte haben,
die größer als die Durchschnittsdichte der Flüssigkeit ist, und wo Komplexe der einen
oder mehreren Zielzellen und der Selektionsgruppe anschließend auf die Detektionsoberfläche
unter Anwendung von Gravitations-, Zentrifugal- oder Zentripetalkraft abgesetzt werden.
37. Verfahren nach Anspruch 1, wo die Zielzellen in der Detektionszone unter Anwendung
eines Selektionsverfahrens abgesetzt werden, das aus der Gruppe bestehend aus magnetischer
Selektion, Zentrifugation, Ablagerung und Filtration ausgewählt ist, wo eine Selektionsgruppe
nicht angewendet wird.
38. Verfahren nach Anspruch 1, wo, vor Schritt (a), die Probe behandelt wird, um die Probe
zu verflüssigen und/oder zu homogenisieren.
39. Verfahren nach Anspruch 1, wo, vor Schritt (a), die Probe behandelt wird, um andere
Stoffe oder Gegenstände als die Zielzellen zu entfernen.
40. Verfahren nach Anspruch 1, weiterhin umfassend den Schritt zur Bestimmung der Wirkung
von einem oder mehreren Stoffen oder einer oder mehreren Behandlungen auf eine oder
mehrere Merkmale der Zielzellen.
41. Verfahren nach Anspruch 40, wo das Merkmal die Fähigkeit ist, Zellreplikation durchzumachen.
42. Verfahren nach Anspruch 40, wo der eine oder mehreren Stoffe in einem Medium vorkommen,
das für die Unterstützung der Replikation der Zielzellen verwendet wird.
43. Verfahren nach Anspruch 40, wo das Merkmal die Fähigkeit der Zielzellen ist, nach
einer Sterilisationsbehandlung zu replizieren.
44. Verfahren nach Anspruch 40, wo das Merkmal die Fähigkeit der Zielzellen ist, in Gegenwart
von einem oder mehreren potenziellen Replikationsinhibitoren zu replizieren.
45. Verfahren nach Anspruch 40, wo die Zielzellen Bakterien, Pilze, Parasiten oder gezüchtete
Zellen sind, und die Stoffe antibakterielle Mittel, Mittel, Antipilzmittel oder Antiparasitenmittel
sind.
46. Verfahren nach Anspruch 44, wo der eine oder mehreren Inhibitoren antimikrobielle
Verbindungen sind.
47. Verfahren nach Anspruch 44, wo der eine oder mehreren Inhibitoren Antitumorverbindungen
sind.
48. Verfahren nach Anspruch 40, wo das Merkmal die Lebensfähigkeit, Veränderung einer
optischen Eigenschaft, metabolische oder enzymatische Aktivität oder biochemische
Konstituenten der Zielzellen ist.
49. Verfahren nach Anspruch 34, weiterhin umfassend die folgenden Schritte:
(d) Kontaktieren der Zielzellen mit einem oder mehreren Stoffen oder Behandlung der
Zellen mit einer oder mehreren Behandlungen; und
(e) Bestimmen der Wirkung von dem einen oder mehreren Stoffen oder der einen oder
mehreren Behandlungen auf ein oder mehrere Merkmale der Zielzellen.
50. Verfahren nach Anspruch 1, wo die Detektionszone ein Material umfasst, das aus der
Gruppe bestehend aus Glas, Kunststoff, der Oberfläche von Brunnen in Mikrotiterplatten,
saugfähigen Membranen, Kunststoffstreifen, den Oberflächen von Kapillaren, den Oberflächen
von Mikrofluidkammern und den Oberflächen von Mikrofluidkanalen ausgewählt ist.
51. Verfahren nach Anspruch 1, wo die Replikation der Zellen in den Mikrokolonien nach
dem Detektieren fortgesetzt wird.
52. Verfahren nach Anspruch 1, wo Schritt (c) mindestens zwei Zyklen umfasst, die jeweils
eine Periode umfasst, wo die Replizierung der Zellen nach einem Detektionsschritt
ermöglicht wird.
53. Verfahren nach Anspruch 1, weiterhin umfassend den Schritt zum Wiederholen von Schritt
(a) - (c) mit einer oder mehreren zusätzlichen Proben, wo das Wiederholen automatisiert
ist.
54. Verfahren nach Anspruch 53, wo die Proben automatisch in ein Instrument, das einen
Detektor umfasst, geladen werden.
55. Verfahren nach Anspruch 53, wo die Proben automatisch in einer Reihe von Detektionszonen
abgesetzt werden, die physisch verbunden sind und automatisch und nacheinander in
ein Instrument, das einen Detektor umfasst, geladen werden.
56. Verfahren nach Anspruch 1, wo das Dektektieren Beleuchten von einer oder mehreren
Mikrokolonien umfasst, um ein nachweisbares Signal zu erzeugen.
57. Verfahren nach Anspruch 56, wo Licht, das als Ergebnis des Beleuchtens der einen oder
mehreren Mikrokolonien emittiert, verstreut, reflektiert oder absorbiert wurde, beim
Detektieren detektiert wird.
58. Verfahren nach Anspruch 1, wo Fluoreszenz beim Detektieren detektiert wird.
59. Verfahren nach Anspruch 58, wo die Fluoreszenz Autofluoreszenz ist, die von den Mikrokolonien
emittiert wird.
60. Verfahren nach Anspruch 56, wo ein oder mehrere Lasers beim Beleuchten angewendet
werden.
61. Verfahren nach Anspruch 56, wo eine oder mehrere lichtemittierende Dioden beim Beleuchten
angewendet werden.
62. Verfahren nach Anspruch 56, wo eine Weißlichtquelle beim Beleuchten angewendet wird.
63. Verfahren nach Anspruch 56, wo das Beleuchten durch ein oder mehrere optische Filter,
die nur ausgewählte Lichtwellenlängen durchpassieren lassen, erfolgt.
64. Verfahren nach Anspruch 1, weiterhin umfassend den Schritt, wo vor oder während Schritt
(c) die Probe mit einer Signalgruppe, die entweder direkt oder indirekt mit den Zielzellen
verbunden ist, kontaktiert wird.
65. Verfahren nach Anspruch 64, wo die Signalgruppe mit einem Kategoriebindungsmolekül
verbunden ist.
66. Verfahren nach Anspruch 1, weiterhin umfassend den Schritt, wo vor oder während Schritt
(c) die Probe mit einem Kategoriebindungsmolekül unter Bedingungen, die die Bildung
von einem oder mehreren Komplexen zwischen dem Kategoriebindungsmolekül und einer
oder mehreren kategoriespezifischen Bindungsstellen an einer oder mehreren der Zielzellen
ermöglichen, kontaktiert wird.
67. Verfahren nach Anspruch 66, wo das Kategoriebindungsmolekül einen Antikörper, Aptamer
oder Ligand umfasst.
68. Verfahren nach Anspruch 66, wo das Kategoriebindungsmolekül entweder direkt oder indirekt
mit einer oder mehreren Signalgruppen markiert ist.
69. Verfahren nach Anspruch 68, weiterhin umfassend den Schritt, vor Schritt (c) irgendein
Kategoriebindungsmolekül von dem einen oder mehreren Komplexen zu entfernen.
70. Verfahren nach Anspruch 66, wo das Kategoriebindungsmolekül Mitglied einer Sammlung
von Kategoriebindungsmolekülen ist, wo die Sammlung eine Familie von Kategoriebindungsmolekülen,
die für jede nicht-überlappende Kategorie von zu detektierenden Zielzellen spezifisch
ist, umfasst.
71. Verfahren nach Anspruch 70, wo jede der Familien von Kategoriebindungsmolekülen mit
einer Signalgruppe markiert ist, die ein Signal einer verschiedenen Signalklasse oder
Signalsignatur ausgibt.
72. Verfahren nach Anspruch 65, wo die Signalgruppe ein Partikel ist oder mit einem Partikel
physisch verbunden ist.
73. Verfahren nach Anspruch 64, wo die Signalgruppe fluoreszierende Signalbeschaffenheit
hat.
74. Verfahren nach Anspruch 73, wo die Signalgruppe aus der Gruppe bestehend aus organischen
Fluorophoren, hochregulierten Phosphors, Lanthaniden, Quantenpunkten, Enzymen, die
fluoreszierende Produkte von nichtfluoreszierenden Substraten erzeugen, und fluoreszenzgefärbten
Partikeln ausgewählt ist.
75. Verfahren nach Anspruch 73, wo die Signalgruppe ein fluoreszierendes Färbemittel für
Zellen ist.
76. Verfahren nach Anspruch 64, wo die Signalgruppe chromogene Signalbeschaffenheit hat.
77. Verfahren nach Anspruch 64, wo die Signalgruppe chemilumineszierende Signalbeschaffenheit
hat.
78. Verfahren nach Anspruch 64, wo die Signalgruppe lichtstreuende Signalbeschaffenheit
hat.
79. Verfahren nach Anspruch 78, wo die Signalgruppe ein resonanzlichtstreuendes Partikel
oder ein Plasmonresonanzpartikel ist.
80. Verfahren nach Anspruch 64, wo die Signalgruppe eine Vitalitätsfärbung zum Färben
lebendiger Zellen ist.
81. Verfahren nach Anspruch 70, wo die Sammlung von Kategoriebindungsmolekülen eine Familienkomplexität
von 1 hat.
82. Verfahren nach Anspruch 70, wo die Sammlung von Kategoriebindungsmolekülen eine Familienkomplexität
hat, die größer als 1 ist.
83. Verfahren nach Anspruch 82, wo die Sammlung eine Familienkomplexität ≥ 5 hat.
84. Verfahren nach Anspruch 64, wo die Signalgruppe eine oder mehrere Verbindungen umfasst,
die erst nachweisbar sind, wenn sie in Verbindung mit den Zielzellen kommen, wobei
die Signalgruppe von einem Bestandteil der Zielzellen oder von einem physiologischen,
physischen oder mikroumgebungsmäßigen Zustand der Zielzellen beeinflusst wird.
85. Verfahren nach Anspruch 1, wo die Replikation und das Detektieren in einem Behälter
erfolgen, der derart konstruiert ist, dass zusätzliche Zellen nicht eintreten oder
Zellen in der Probe nicht austreten können.
86. Verfahren nach Anspruch 1, wo die Replikation und das Detektieren in einem Behälter
erfolgen, der einen Barcode oder eine entsprechende Markierung zum automatischen Verfolgen
der Probe hat.
87. Verfahren nach Anspruch 1, wo die Replikation und das Detektieren auf einer Oberfläche
mit Registrierungsmarken erfolgen, um Anpassung von mehreren Bildern derselben Oberfläche
zu erleichtern.
88. Verfahren nach Anspruch 1, wo Kontrollmarken oder Kontrollzellen in einem vorgegebenen
Bereich der Detektionszone beim Detektieren detektiert werden.
89. Verfahren nach Anspruch 1, wo optische Filter, die dafür angepasst sind, ein Signal
von der Illumination der Zielzellen zu detektieren, beim Detektieren angewendet werden.
90. Verfahren nach Anspruch 1, wo ein fotoelektrischer Detektor beim Detektieren angewendet
wird.
91. Verfahren nach Anspruch 1, wo ein fotoelektrischer Array-Detektor beim Detektieren
angewendet wird.
92. Verfahren nach Anspruch 91, wo der fotoelektrische Detektor einen CCD-Detektor umfasst.
93. Verfahren nach Anspruch 1, wo kein Bildverstärker beim Detektieren angewendet wird.
94. Verfahren nach Anspruch 1, wo ein Fotovervielfacherröhredetektor beim Detektieren
angewendet wird.
95. Verfahren nach Anspruch 1, wo ein Fotodiodedetektor beim Detektieren angewendet wird.
96. Verfahren nach Anspruch 1, wo ein fotoempfindlicher Film beim Detektieren angewendet
wird.
97. Verfahren nach Anspruch 1, weiterhin umfassend den Schritt, wo während oder nach Schritt
(c), die Zahl der Mikrokolonien quantifiziert wird.
98. Verfahren nach Anspruch 1, weiterhin umfassend den Schritt, wo während oder nach Schritt
(c), die Kategorie der Zielzellen durch Analysieren eines Bildes aus dem Detektionsbereich
unter Anwendung der Bildanalyse-Software bestimmt wird.
99. Verfahren nach Anspruch 1, weiterhin umfassend den Schritt, wo während oder nach Schritt
(c), die Stellen der einen oder mehreren Mikrokolonien in der Detektionszone durch
Analysieren eines Bildes aus dem Detektionsbereich unter Anwendung der Bildanalyse-Software
bestimmt wird.
100. Verfahren nach Anspruch 99, weiterhin umfassend den Schritt, wo während oder nach
Schritt (c), die Stellen individueller Mikrokolonien in der Detektionszone mit vorher
festgelegten Stellen derselben Mikrokolonien verglichen werden.
101. Verfahren nach Anspruch 100, wo die Bildanalyse-Software Algorithmen zum Unterscheiden
zwischen Gegenständen, die über Zeit Größe ändern, und Gegenständen, die über Zeit
nicht Größe ändern, umfasst.
102. Verfahren nach Anspruch 97, wo die Bestimmung Analysieren eines Bildes aus dem Detektionsbereich
umfasst.
103. Verfahren nach Anspruch 43, wo die Sterilisationsbehandlung aus der Gruppe bestehend
aus Wärmesterilisation, Bestrahlung, Exponierung von toxischem Gas und Desinfektionsmittelbehandlung
ausgewählt ist.
1. Une méthode de détection de cellules cibles vivantes dans un échantillon, ladite méthode
comprenant les étapes suivantes :
(a) dépôt des cellules cibles vivantes présentes dans ledit échantillon dans une zone
de détection comprenant une surface de détection, à raison d'une densité inférieure
à 100 cellules cibles par mm2 de la surface de détection, lesdites cellules étant dispersées et immobilisées de
façon aléatoire dans ladite zone de détection ;
(b) formation d'une ou plusieurs microcolonies desdites cellules cibles par réplication
in situ ; et
(c) détection desdites une ou plusieurs microcolonies ;
dans laquelle la plus longue dimension linéaire de ladite surface de détection est
supérieure à 1 mm ; lesdites une ou plusieurs microcolonies mesurent en moyenne moins
de 50 microns dans au moins deux dimensions orthogonales ; ladite détection n'implique
pas un grossissement supérieur à 5x ; ladite détection détecte une propriété desdites
une ou plusieurs microcolonies qui ne dépend pas de l'adjonction d'un groupe de signalisation
ou d'une molécule se liant à une catégorie ; et lesdites cellules dans lesdites une
ou plusieurs microcolonies restent capables de se répliquer après ladite détection.
2. La méthode de la revendication 1, dans laquelle lesdites cellules cibles sont dispersées
de façon aléatoire dans une zone de détection à raison d'une densité inférieure à
10 cellules cibles par mm2 de la surface de détection.
3. La méthode de la revendication 1, dans laquelle lesdites cellules cibles sont dispersées
de façon aléatoire dans une zone de détection à raison d'une densité inférieure à
1 cellule cible par mm2 de la surface de détection.
4. La méthode de la revendication 1, dans laquelle ladite détection détecte une seule
microcolonie dans la surface de détection.
5. La méthode de la revendication 1, dans laquelle ladite détection détecte des microcolonies
chevauchantes ou contiguës.
6. La méthode de la revendication 1, dans laquelle ladite détection n'implique pas un
grossissement supérieur à 2x.
7. La méthode de la revendication 1, dans laquelle ladite détection n'implique pas un
grossissement supérieur à 1x.
8. La méthode de la revendication 1, dans laquelle ladite détection n'implique pas un
grossissement supérieur à 0,2x.
9. La méthode de la revendication 1, dans laquelle le nombre moyen de cellules dans lesdites
une ou plusieurs microcolonies est inférieur à 50 000 cellules.
10. La méthode de la revendication 9, dans laquelle lesdites une ou plusieurs microcolonies
comprennent moins de 10 000 cellules.
11. La méthode de la revendication 9, dans laquelle le nombre moyen de cellules dans lesdites
une ou plusieurs microcolonies est inférieur à 1 000.
12. La méthode de la revendication 9, dans laquelle le nombre moyen de cellules dans lesdites
une ou plusieurs microcolonies est inférieur à 100.
13. La méthode de la revendication 9, dans laquelle le nombre moyen de cellules dans lesdites
une ou plusieurs microcolonies est inférieur à 10.
14. La méthode de la revendication 9, dans laquelle lesdites une ou plusieurs microcolonies
mesurent en moyenne moins de 25 microns dans leur plus longue dimension linéaire.
15. La méthode de la revendication 9, dans laquelle lesdites une ou plusieurs microcolonies
mesurent en moyenne moins de 10 microns dans leur plus longue dimension linéaire.
16. La méthode de la revendication 1, dans laquelle lesdites cellules cibles sont des
bactéries.
17. La méthode de la revendication 1, dans laquelle lesdites cellules cibles sont des
cellules eucaryotes.
18. La méthode de la revendication 17, dans laquelle lesdites cellules cibles sont des
cellules de moisissures ou fongiques.
19. La méthode de la revendication 17, dans laquelle lesdites cellules cibles sont des
cellules humaines, animales ou végétales.
20. La méthode de la revendication 1, dans laquelle lesdites cellules cibles sont des
parasites de l'homme, des animaux ou des végétaux.
21. La méthode de la revendication 1, dans laquelle ladite détection détecte et identifie
des microcolonies de plusieurs catégories de cellules non chevauchantes.
22. La méthode de la revendication 1, dans laquelle ledit échantillon comprend des fluides
ou des tissus provenant d'un organisme pluricellulaire.
23. La méthode de la revendication 1, dans laquelle ledit échantillon comprend les fluides
ou tissus corporels d'un animal.
24. La méthode de la revendication 1, dans laquelle ledit échantillon est issu d'un être
humain.
25. La méthode de la revendication 1, dans laquelle ledit échantillon est issu d'un vertébré
non humain.
26. La méthode de la revendication 1, dans laquelle ledit échantillon est choisi dans
le groupe consistant en : échantillons de tractus respiratoire, urogénital, reproducteur,
de système nerveux central, d'urine, de sang, de derme, de plasma, de sérum, de salive,
de tissus d'une plaie, d'exsudat d'une plaie, de biopsie, de fèces, d'appareil reproducteur
et de tissus solides, et dérivés de ceux-ci.
27. La méthode de la revendication 1, dans laquelle ledit échantillon est un échantillon
de sang ou d'urine.
28. La méthode de la revendication 1, dans laquelle ledit échantillon est issu d'un végétal.
29. La méthode de la revendication 1, dans laquelle ledit échantillon est obtenu par échantillonnage
d'air ou d'eau provenant de l'environnement, ou de surfaces, d'objets ou d'organismes
exposés à l'environnement.
30. La méthode de la revendication 1, dans laquelle ledit échantillon provient d'une matière
choisie dans le groupe consistant en : matière première, intermédiaire ou finale de
la fabrication de produits pharmacologiques, cosmétiques, sanguins ou autres produits
à usage local ou interne chez l'homme ou chez l'animal ; matière première, intermédiaire
ou finale de la fabrication d'aliments ou de boissons ; matière première, intermédiaire
ou finale de la fabrication de dispositifs médicaux ou de diagnostic in vitro ; produits chimiques ; surfaces industrielles ; instrumentation; et machines.
31. La méthode de la revendication 1, dans laquelle ladite zone de détection est mise
au contact d'un milieu liquide comprenant une ou plusieurs substances facilitant la
réplication des cellules cibles.
32. La méthode de la revendication 1, dans laquelle lesdites cellules sont déposées directement
sur un milieu de culture solide ou semi-solide.
33. La méthode de la revendication 1, dans laquelle, avant l'étape (a), une méthode de
sélection est utilisée pour déposer des complexes formés par une ou plusieurs desdites
cellules cibles et un groupe de sélection dans ladite zone de détection, dans laquelle
ladite méthode de sélection est choisie dans le groupe consistant en : sélection magnétique,
centrifugation, sédimentation et filtration.
34. La méthode de la revendication 33, dans laquelle, avant l'étape (a), lesdites cellules
cibles sont mises au contact de groupes de sélection magnétique spécifiques des cellules
cibles et des complexes formés par une ou plusieurs desdites cellules cibles et ledit
groupe de sélection sont ensuite déposés sur ladite surface de détection au moyen
d'une force magnétique.
35. La méthode de la revendication 34, dans laquelle lesdits groupes de sélection magnétique
spécifiques des cellules cibles comprennent des particules magnétiques qui sont conjuguées
à des molécules se liant à une catégorie.
36. La méthode de la revendication 33, dans laquelle lesdites cellules cibles sont mises
au contact, dans un liquide, avec lesdits groupes de sélection spécifiques des cellules
cibles, qui présentent une densité moyenne supérieure à la densité moyenne dudit liquide,
et dans laquelle les complexes formés par une ou plusieurs desdites cellules cibles
et ledit groupe de sélection sont ensuite déposés sur ladite surface de détection
au moyen d'une force gravitationnelle, centrifuge ou centripète.
37. La méthode de la revendication 1, dans laquelle lesdites cellules cibles sont déposées
dans ladite zone de détection en utilisant une méthode de sélection choisie dans le
groupe consistant en : sélection magnétique, centrifugation, sédimentation et filtration,
dans laquelle aucun groupe de sélection n'est utilisé.
38. La méthode de la revendication 1, dans laquelle, avant l'étape (a), ledit échantillon
est traité afin de liquéfier et/ou d'homogénéiser ledit échantillon.
39. La méthode de la revendication 1, dans laquelle, avant l'étape (a), ledit échantillon
est traité afin d'éliminer les substances ou objets autres que lesdites cellules cibles.
40. La méthode de la revendication 1, comprenant également une étape de détermination
de l'effet d'un(e) ou plusieurs substances ou traitements sur un ou plusieurs attributs
desdites cellules cibles.
41. La méthode de la revendication 40, dans laquelle ledit attribut est l'aptitude à subir
une réplication cellulaire.
42. La méthode de la revendication 40, dans laquelle lesdites une ou plusieurs substances
sont présentes dans un milieu utilisé pour favoriser la réplication desdites cellules
cibles.
43. La méthode de la revendication 40, dans laquelle ledit attribut est l'aptitude desdites
cellules cibles à se répliquer après un traitement de stérilisation.
44. La méthode de la revendication 40, dans laquelle ledit attribut est l'aptitude desdites
cellules cibles à se répliquer en présence d'un ou plusieurs inhibiteurs potentiels
de la réplication.
45. La méthode de la revendication 40, dans laquelle lesdites cellules cibles sont des
bactéries, des moisissures, des parasites ou des cultures cellulaires, et lesdites
substances sont des agents antibactériens, des agents antifongiques ou des agents
antiparasitaires.
46. La méthode de la revendication 44, dans laquelle lesdits un ou plusieurs inhibiteurs
sont des composés antimicrobiens.
47. La méthode de la revendication 44, dans laquelle lesdits un ou plusieurs inhibiteurs
sont des composés antitumoraux.
48. La méthode de la revendication 40, dans laquelle ledit attribut est la viabilité,
le changement d'une propriété optique, l'activité métabolique ou enzymatique, ou la
constitution biochimique desdites cellules cibles.
49. La méthode de la revendication 34, comprenant également les étapes suivantes :
(d) mise en contact desdites cellules cibles avec une ou plusieurs substances ou traitement
desdites cellules par un ou plusieurs traitements ; et
(e) détermination de l'effet desdites ou plusieurs substances ou desdits un ou plusieurs
traitements sur un ou plusieurs attributs desdites cellules cibles.
50. La méthode de la revendication 1, dans laquelle ladite zone de détection comprend
une matière choisie dans le groupe consistant en : verre, plastique, surface des puits
de plaques microtitres, membranes hydrophiles, bandes de plastique, surfaces de tubes
capillaires, surfaces de chambres microfluidiques et surfaces de canaux microfluidiques.
51. La méthode de la revendication 1, dans laquelle la réplication desdites cellules dans
lesdites microcolonies se poursuit après ladite détection.
52. La méthode de la revendication 1, dans laquelle l'étape (c) comprend au moins deux
cycles dont chacun comporte une période durant laquelle on laisse les cellules se
répliquer suivie d'une étape de détection.
53. La méthode de la revendication 1, comprenant également une étape de répétition des
étapes (a) à (c) avec un ou plusieurs échantillons supplémentaires, dans laquelle
ladite répétition est automatisée.
54. La méthode de la revendication 53, dans laquelle lesdits échantillons sont chargés
automatiquement dans un instrument comprenant un détecteur.
55. La méthode de la revendication 53, dans laquelle lesdits échantillons sont déposés
automatiquement dans une série de zones de détection qui sont physiquement associées
et qui sont automatiquement et successivement chargées dans un instrument comprenant
un détecteur.
56. La méthode de la revendication 1, dans laquelle ladite détection comprend l'illumination
d'une ou plusieurs microcolonies pour générer un signal détectable.
57. La méthode de la revendication 56, dans laquelle ladite détection détecte la lumière
émise, diffusée, réfléchie ou absorbée à la suite de l'illumination desdites une ou
plusieurs microcolonies.
58. La méthode de la revendication 1, dans laquelle ladite détection détecte une fluorescence.
59. La méthode de la revendication 58, dans laquelle ladite fluorescence est une autofluorescence
émise par lesdites microcolonies.
60. La méthode de la revendication 56, dans laquelle ladite illumination utilise un ou
plusieurs lasers.
61. La méthode de la revendication 56, dans laquelle ladite illumination utilise une ou
plusieurs diodes électroluminescentes.
62. La méthode de la revendication 56, dans laquelle ladite illumination utilise une source
de lumière blanche.
63. La méthode de la revendication 56, dans laquelle ladite illumination se fait au travers
d'un ou plusieurs filtres optiques qui ne laissent passer que certaines longueurs
d'ondes lumineuses.
64. La méthode de la revendication 1, comprenant également, avant ou pendant l'étape (c),
une étape de mise en contact dudit échantillon avec un groupe de signalisation qui
s'associe directement ou indirectement avec lesdites cellules cibles.
65. La méthode de la revendication 64, dans laquelle ledit groupe de signalisation est
associé avec une molécule se liant à une catégorie.
66. La méthode de la revendication 1, comprenant également, avant ou pendant l'étape (c),
une étape de mise en contact dudit échantillon avec une molécule se liant à une catégorie
dans des conditions permettant la formation d'un ou plusieurs complexes entre ladite
molécule se liant à une catégorie et un ou plusieurs sites de liaison spécifiques
d'une catégorie sur une ou plusieurs desdites cellules cibles.
67. La méthode de la revendication 66, dans laquelle ladite molécule se liant à une catégorie
comprend un anticorps, un aptamère ou un ligand.
68. La méthode de la revendication 66, dans laquelle ladite molécule se liant à une catégorie
est marquée, directement ou indirectement, par un ou plusieurs groupes de signalisation.
69. La méthode de la revendication 68, comprenant également, avant l'étape (c), une étape
consistant à éliminer desdits un ou plusieurs complexes toute molécule se liant à
une catégorie qui n'est pas liée.
70. La méthode de la revendication 66, dans laquelle ladite molécule se liant à une catégorie
appartient à une collection de molécules se liant à une catégorie, dans laquelle ladite
collection comprend une famille de molécules se liant à une catégorie spécifique pour
chaque catégorie non chevauchante de cellules cibles à détecter.
71. La méthode de la revendication 70, dans laquelle chacune desdites familles de molécules
se liant à une catégorie est marquée par un groupe de signalisation qui émet un signal
correspondant à une classe de signaux ou à une signature de signal distincte.
72. La méthode de la revendication 65, dans laquelle ledit groupe de signalisation est
une particule ou est physiquement associé à une particule.
73. La méthode de la revendication 64, dans laquelle ledit groupe de signalisation présente
un caractère de signalisation par fluorescence.
74. La méthode de la revendication 73, dans laquelle ledit groupe de signalisation est
choisi dans le groupe consistant en : fluorophores organiques, phosphores régulés
à la hausse, lanthanides, points quantiques, enzymes générant un produit fluorescent
à partir de substrats non fluorescents, et particules teintées par fluorescence.
75. La méthode de la revendication 73, dans laquelle ledit groupe de signalisation est
un colorant fluorescent pour cellules.
76. La méthode de la revendication 64, dans laquelle ledit groupe de signalisation présente
un caractère de signalisation chromogène.
77. La méthode de la revendication 64, dans laquelle ledit groupe de signalisation présente
un caractère de signalisation par chimiluminescence.
78. La méthode de la revendication 64, dans laquelle ledit groupe de signalisation présente
un caractère de signalisation par diffusion de la lumière.
79. La méthode de la revendication 78, dans laquelle ledit groupe de signalisation est
une particule de diffusion résonnante de la lumière ou une particule à résonance plasmonique.
80. La méthode de la revendication 64, dans laquelle ledit groupe de signalisation est
un colorant de viabilité pour la coloration de cellules vivantes.
81. La méthode de la revendication 70, dans laquelle ladite collection de molécules se
liant à une catégorie présente une complexité familiale égale à 1.
82. La méthode de la revendication 70, dans laquelle ladite collection de molécules se
liant à une catégorie présente une complexité familiale supérieure à 1.
83. La méthode de la revendication 82, dans laquelle ladite collection présente une complexité
familiale ≥ 5.
84. La méthode de la revendication 64, dans laquelle ledit groupe de signalisation comprend
un ou plusieurs composés qui ne sont pas détectables tant que, après association avec
lesdites cellules cibles, ledit groupe de signalisation subit l'action d'un constituant
desdites cellules cibles ou d'un état physiologique, physique ou microenvironnemental
desdites cellules cibles.
85. La méthode de la revendication 1, dans laquelle ladite réplication et ladite détection
ont lieu dans un récipient construit de manière à ne pas permettre la pénétration
de cellules supplémentaires ni la sortie de cellules de l'échantillon.
86. La méthode de la revendication 1, dans laquelle ladite réplication et ladite détection
ont lieu dans un récipient possédant un code-barres ou un marquage équivalent permettant
de suivre automatiquement l'échantillon.
87. La méthode de la revendication 1, dans laquelle ladite réplication et ladite détection
ont lieu sur une surface portant des marques de repérage pour faciliter l'alignement
de plusieurs images de la même surface.
88. La méthode de la revendication 1, dans laquelle ladite détection détecte des marques
de contrôle ou des cellules témoins dans une région définie de la zone de détection.
89. La méthode de la revendication 1, dans laquelle ladite détection utilise des filtres
optiques adaptés pour détecter un signal consécutif à l'illumination desdites cellules
cibles.
90. La méthode de la revendication 1, dans laquelle ladite détection utilise un détecteur
photoélectrique.
91. La méthode de la revendication 1, dans laquelle ladite détection utilise un détecteur
à éléments photoélectriques.
92. La méthode de la revendication 91, dans laquelle ledit détecteur photoélectrique comprend
un détecteur à couplage de charge.
93. La méthode de la revendication 1, dans laquelle ladite détection n'utilise pas d'intensificateur
d'image.
94. La méthode de la revendication 1, dans laquelle ladite détection utilise un détecteur
à tube photomultiplicateur.
95. La méthode de la revendication 1, dans laquelle ladite détection utilise un détecteur
à photodiode.
96. La méthode de la revendication 1, dans laquelle ladite détection utilise un film photosensible.
97. La méthode de la revendication 1, comprenant également, pendant ou après l'étape (c),
une étape de quantification du nombre de microcolonies.
98. La méthode de la revendication 1, comprenant également, pendant ou après l'étape (c),
une étape de détermination de la catégorie desdites cellules cibles par analyse d'une
image de ladite surface de détection au moyen d'un logiciel d'analyse d'images.
99. La méthode de la revendication 1, comprenant également, pendant ou après l'étape (c),
une étape de détermination de l'emplacement desdites une ou plusieurs microcolonies
dans la zone de détection par analyse d'une image de ladite surface de détection au
moyen d'un logiciel d'analyse d'images.
100. La méthode de la revendication 99, comprenant également, pendant ou après l'étape
(c), une étape de comparaison desdits emplacements des différentes microcolonies dans
la zone de détection avec les emplacements précédemment déterminés des mêmes microcolonies.
101. La méthode de la revendication 100, dans laquelle ledit logiciel d'analyse d'images
comprend des algorithmes permettant de discerner les objets qui changent de taille
dans le temps des objets qui ne changent pas de taille dans le temps.
102. La méthode de la revendication 97, dans laquelle ladite détermination comprend l'analyse
d'une image de ladite surface de détection.
103. La méthode de la revendication 43, dans laquelle ledit traitement de stérilisation
est choisi dans le groupe consistant en : stérilisation par la chaleur, irradiation,
exposition à un gaz toxique, et traitement par un désinfectant.